ztSim - Zero Trust Simulation Framework

A comprehensive simulation framework for comparing zero-trust security models, specifically designed to demonstrate that the ADP (Adaptive) model is more secure than the BAS (Baseline) model without sacrificing usability.

🎯 Project Overview

This project implements a sophisticated zero-trust simulation environment that models user behavior, authentication challenges, and security policies in enterprise networks. The simulation compares two zero-trust models:

BAS (Baseline): Traditional zero-trust model with static policies
ADP (Adaptive): Enhanced zero-trust model with dynamic trust factors and adaptive mechanisms

Key Research Question

"Can we enhance security without sacrificing usability in zero-trust architectures?"

The simulation demonstrates that ADP successfully breaks the traditional security-usability trade-off paradigm, providing:

Significant reduction in safe moves (better security - fewer opportunities for attackers)
Fewer completed attacks (better threat prevention)
Improved auth challenge efficiency for completed attacks (maintained usability)
No usability degradation for legitimate users (comparable or improved experience)

🚀 Key Features

Simulation Capabilities

Multi-parameter experimentation across network configurations
Probabilistic user behavior modeling with Bernoulli distributions
Dynamic policy graph generation with configurable resource networks
Adaptive trust factor mechanisms with real-time penalty calculations
Attack sequence simulation for security evaluation
Comprehensive data collection with 20+ metrics per simulation run

Analysis & Visualization

Publication-quality visualizations demonstrating key narrative
Statistical analysis with significance testing
Interactive Jupyter notebooks for exploratory analysis
Automated report generation with key findings
Parameter sensitivity analysis across all configuration dimensions

Configuration Management

JSON-based configuration system for easy parameter modification
Modular architecture supporting different simulation modes
Reproducible experiments with deterministic seeding
Scalable execution across parameter combinations

📋 Prerequisites

System Requirements

Python 3.13+ (required for latest features)
8GB+ RAM (recommended for large-scale simulations)
2GB+ disk space (for simulation data and visualizations)

Dependencies

The project uses Poetry for dependency management with core packages:

pandas (2.3.2+) - Data manipulation and analysis
matplotlib (3.10.5+) - Visualization and plotting
seaborn (0.13.2+) - Statistical visualization
numpy (2.3.2+) - Numerical computing
scipy (1.16.2+) - Scientific computing
networkx (3.5+) - Graph/network analysis
ipykernel (6.30.1+) - Jupyter notebook support

🛠️ Installation

1. Clone the Repository

git clone <repository-url>cd ztSim

2. Install Poetry (if not already installed)

curl -sSL https://install.python-poetry.org | python3 -

3. Install Dependencies

poetry install

4. Activate Virtual Environment

poetry shell

🎮 Quick Start

Run Complete Analysis (Recommended)

# Analyze existing simulation data and generate visualizations
python src/analysis/analysis.py
# Run security analysis (challenge efficiency and proactive defense)
python src/analysis/security_analysis.py

This will:

Load simulation records from the archive directory
Generate publication-quality visualizations demonstrating the narrative
Display interactive plots with plt.show()
Save all outputs to src/analysis/plots/
Print statistical summaries supporting the narrative

Run New Simulations

Standard User Behavior Simulation

# Generate new BAS and ADP simulation data
python src/base_runner.py

Attack Sequence Simulation

# Simulate attack scenarios for security evaluation
python src/attack_runner.py

Interactive Analysis

# Launch Jupyter for custom analysis
jupyter lab src/analysis/analyzer.ipynb
jupyter lab src/analysis/att_analyzer.ipynb

📊 Usage Examples

Basic Simulation Execution

The simulation automatically runs across all parameter combinations defined in configuration files:

# Parameters are loaded from src/assets/config/*.json# - num_resources: [250, 500, 750, 1000, 1250, 1500]# - num_steps: [12, 16, 20, 24, 28, 32]# - edge_density: [0.5, 0.4, 0.3, 0.2, 0.1]# - p_val: [0.9, 0.8, 0.6, 0.5, 0.4, 0.2, 0.1]# - threshold: [4.0, 5.0, 6.0, 7.0, 8.0]# This generates 6 × 6 × 5 × 7 × 5 × 2 = 12,600 simulation runs# (2 modes: BAS and ADP)

Custom Parameter Configuration

Modify configuration files in src/assets/config/ to customize simulations:

# Edit basic simulation parameters
vim src/assets/config/basic_config.json
# Edit adaptive model parameters
vim src/assets/config/adaptive_config.json
# Edit network topology parameters
vim src/assets/config/policy_graph_config.json

Analysis Customization

fromsrc.analysis.analysisimportSecurityUsabilityAnalyzer# Initialize analyzeranalyzer=SecurityUsabilityAnalyzer()
# Load custom data filesanalyzer.load_and_merge_data(
bas_file="path/to/custom_bas_data.csv",
adp_file="path/to/custom_adp_data.csv"
)
# Generate specific analysisanalyzer.create_narrative_visualization()

📁 Project Structure

ztSim/
├── README.md # This file - project overview and setup
├── pyproject.toml # Poetry configuration and dependencies
├── src/ # Source code directory
│ ├── README.md # Source code documentation
│ ├── analysis/ # Analysis tools and visualizations
│ │ ├── README.md # Analysis documentation
│ │ ├── analysis.py # Main thesis analysis script
│ │ ├── security_analysis.py # Strategic security analysis script
│ │ ├── analyzer.ipynb # Interactive analysis notebook
│ │ ├── att_analyzer.ipynb # Attack analysis notebook
│ │ └── plots/ # Generated visualizations
│ ├── assets/ # Configuration and data files
│ │ ├── config/ # JSON configuration files
│ │ ├── archive/ # Historical simulation datasets (organized by run number)
│ │ └── results/ # New simulation outputs
│ ├── base_runner.py # Main simulation runner (user behavior)
│ ├── attack_runner.py # Attack simulation runner
│ ├── simulator.py # Core simulation engine
│ ├── attack_simulator.py # Attack sequence simulator
│ ├── user.py # User behavior models
│ ├── authenticator.py # Authentication system
│ ├── policy_generator.py # Network policy graph generator
│ ├── delta.py # Trust factor penalty calculations
│ └── utils.py # Utility functions
└── ...

🔬 Key Results & Findings

Thesis Validation: "ADP is More Secure Without Sacrificing Usability"

Our comprehensive analysis of 12,600 simulation runs demonstrates:

🔒 Security Improvements (ADP vs BAS)

Safe Moves: Significant reduction (attackers have less freedom to move undetected)
Attack Completion: Fewer successful attacks (better threat prevention)
Proactive Defense: ADP stops attacks before completion, while BAS allows all attacks to complete

👤 Usability Preservation Through Strategic Design

Smart Challenge Strategy: ADP uses proactive defense (early termination) rather than reactive challenge escalation
Surgical Precision: Authentication challenges are strategically focused where they matter most
Challenge Efficiency: Fewer total challenges with better security outcomes - demonstrating "smarter security"
User Experience: Enhanced security without challenge fatigue for legitimate users

⚙️ Breaking the Trade-off with Strategic Intelligence

Traditional Assumption: More security = worse usability ❌
ADP Innovation: Strategic security + maintained usability ✅
Key Insight: ADP prevents problematic attacks early rather than burdening users with excessive authentication
Result: Surgically precise security interventions that enhance protection while preserving user productivity

Generated Visualizations (10 Total)

From analysis.py (8 plots):

Safe Moves Analysis: Security improvement across all parameters (nr, ns, ed, p, th)
Attack Completion Rates: Threat prevention effectiveness across all parameters
Auth Challenge Analysis: Usability for completed attacks across all parameters
Legitimate User Study: Usability for high P values (0.8, 0.9) - no degradation for trusted users
Attacker Usability Analysis: Usability for low P values (0.1, 0.2)
All Attacks Usability: Overall usability including terminated attacks
Terminated Attacks Usability: Usability for attacks stopped by ADP
Thesis Narrative Figure: Publication-quality 2×2 summary panel

From security_analysis.py (2 plots): 9. Early Detection Analysis: How quickly ADP identifies and stops threats (moves and challenges before action) 10. Challenge Distribution Analysis: Strategic authentication focus patterns (scatter plots showing challenges vs moves)

🔧 Configuration Parameters

Network Topology (`policy_graph_config.json`)

num_resources: [250, 500, 750, 1000, 1250, 1500] - Network size
num_steps: [12, 16, 20, 24, 28, 32] - Workflow complexity
edge_density: [0.5, 0.4, 0.3, 0.2, 0.1] - Network connectivity

User Behavior (`basic_config.json`)

p_val: [0.9, 0.8, 0.6, 0.5, 0.4, 0.2, 0.1] - Legitimacy probability
max_session_len: 50 - Maximum session duration
history_depth: 3 - User history tracking depth

Adaptive Model (`adaptive_config.json`)

trust_factor_threshold: [4.0, 5.0, 6.0, 7.0, 8.0] - Security thresholds
init_trust_factor: 7.0 - Initial user trust
base_trust_factor: 10.0 - Maximum trust level

Authentication (`auth_config.json`)

num_auth_levels: 4 - Authentication complexity levels
auth_prob_config: Level-specific success probabilities
access_denied_limit: 3 - Maximum failed attempts

🧪 Testing & Validation

Run Analysis Tests

# Test the core analysis script with existing data
python src/analysis/analysis.py
# Test the security analysis script
python src/analysis/security_analysis.py
# Expected output: 10 visualizations + statistical summaries# Check: src/analysis/plots/ for generated files

Validate Simulation

# Quick simulation test (modify config for smaller parameter space)
python src/base_runner.py
# Check: src/assets/results/ for new CSV files

Interactive Exploration

# Launch Jupyter for detailed investigation
jupyter lab src/analysis/analyzer.ipynb

📈 Performance & Scalability

Simulation Scale

Current Dataset: 12,600 simulation runs
Parameter Space: 5-dimensional configuration matrix
Execution Time: ~2-4 hours for full parameter sweep
Memory Usage: ~2-4GB peak during large simulations

Analysis Performance

Data Loading: <10 seconds for 12,600 records
Visualization Generation: ~30-60 seconds for all plots
Statistical Calculations: <5 seconds for thesis metrics

🤝 Contributing

Development Setup

# Install development dependencies
poetry install --with dev
# Run code formatting
poetry run black src/
poetry run isort src/
# Run type checking
poetry run mypy src/

Adding New Analysis

Create analysis functions in src/analysis/
Follow existing patterns for data loading and visualization
Update documentation in src/analysis/ANALYSIS_README.md
Test with existing datasets before committing

📚 Documentation

README.md (this file): Project overview and setup
src/README.md: Source code structure and modules
src/analysis/README.md: Analysis tools, methods, and thesis documentation
- Core Analysis (analysis.py): Parameter analysis, usability studies, narrative visualization
- Strategic Security Analysis (security_analysis.py): Early detection and challenge distribution
Jupyter Notebooks: Interactive analysis examples
- src/analysis/analyzer.ipynb: Custom exploratory analysis
- src/analysis/att_analyzer.ipynb: Attack sequence analysis

🐛 Troubleshooting

Common Issues

Import Errors

# Ensure you're in the Poetry environment
poetry shell
# Reinstall dependencies if needed
poetry install --sync

Missing Data Files

# Check if simulation data exists
ls src/assets/archive/
# Run simulations if data is missing
python src/base_runner.py

Visualization Issues

# Install GUI backend for matplotlib
poetry add tkinter # or appropriate backend# Use non-interactive backend if neededexport MPLBACKEND=Agg

📄 License

This project is part of academic research on zero-trust security architectures. Please cite appropriately if using this work in academic publications.

🎯 Next Steps

Run the analysis: python src/analysis/analysis.py
Explore the data: Open src/analysis/analyzer.ipynb
Review findings: Check generated plots in src/analysis/plots/
Customize parameters: Modify files in src/assets/config/
Generate new data: Run python src/base_runner.py

The simulation demonstrates that enhanced security doesn't require usability sacrifice - ADP proves this empirically across 12,600 test scenarios.

ztSim - Zero Trust Simulation Framework

A comprehensive simulation framework for comparing zero-trust security models, specifically designed to demonstrate that the ADP (Adaptive) model is more secure than the BAS (Baseline) model without sacrificing usability.

🎯 Project Overview

This project implements a sophisticated zero-trust simulation environment that models user behavior, authentication challenges, and security policies in enterprise networks. The simulation compares two zero-trust models:

BAS (Baseline): Traditional zero-trust model with static policies
ADP (Adaptive): Enhanced zero-trust model with dynamic trust factors and adaptive mechanisms

Key Research Question

"Can we enhance security without sacrificing usability in zero-trust architectures?"

The simulation demonstrates that ADP successfully breaks the traditional security-usability trade-off paradigm, providing:

Significant reduction in safe moves (better security - fewer opportunities for attackers)
Fewer completed attacks (better threat prevention)
Improved auth challenge efficiency for completed attacks (maintained usability)
No usability degradation for legitimate users (comparable or improved experience)

🚀 Key Features

Simulation Capabilities

Multi-parameter experimentation across network configurations
Probabilistic user behavior modeling with Bernoulli distributions
Dynamic policy graph generation with configurable resource networks
Adaptive trust factor mechanisms with real-time penalty calculations
Attack sequence simulation for security evaluation
Comprehensive data collection with 20+ metrics per simulation run

Analysis & Visualization

Publication-quality visualizations demonstrating key narrative
Statistical analysis with significance testing
Interactive Jupyter notebooks for exploratory analysis
Automated report generation with key findings
Parameter sensitivity analysis across all configuration dimensions

Configuration Management

JSON-based configuration system for easy parameter modification
Modular architecture supporting different simulation modes
Reproducible experiments with deterministic seeding
Scalable execution across parameter combinations

📋 Prerequisites

System Requirements

Python 3.13+ (required for latest features)
8GB+ RAM (recommended for large-scale simulations)
2GB+ disk space (for simulation data and visualizations)

Dependencies

The project uses Poetry for dependency management with core packages:

pandas (2.3.2+) - Data manipulation and analysis
matplotlib (3.10.5+) - Visualization and plotting
seaborn (0.13.2+) - Statistical visualization
numpy (2.3.2+) - Numerical computing
scipy (1.16.2+) - Scientific computing
networkx (3.5+) - Graph/network analysis
ipykernel (6.30.1+) - Jupyter notebook support

🛠️ Installation

1. Clone the Repository

git clone <repository-url>cd ztSim

2. Install Poetry (if not already installed)

curl -sSL https://install.python-poetry.org | python3 -

3. Install Dependencies

poetry install

4. Activate Virtual Environment

poetry shell

🎮 Quick Start

Run Complete Analysis (Recommended)

# Analyze existing simulation data and generate visualizations
python src/analysis/analysis.py
# Run security analysis (challenge efficiency and proactive defense)
python src/analysis/security_analysis.py

This will:

Load simulation records from the archive directory
Generate publication-quality visualizations demonstrating the narrative
Display interactive plots with plt.show()
Save all outputs to src/analysis/plots/
Print statistical summaries supporting the narrative

Run New Simulations

Standard User Behavior Simulation

# Generate new BAS and ADP simulation data
python src/base_runner.py

Attack Sequence Simulation

# Simulate attack scenarios for security evaluation
python src/attack_runner.py

Interactive Analysis

# Launch Jupyter for custom analysis
jupyter lab src/analysis/analyzer.ipynb
jupyter lab src/analysis/att_analyzer.ipynb

📊 Usage Examples

Basic Simulation Execution

The simulation automatically runs across all parameter combinations defined in configuration files:

# Parameters are loaded from src/assets/config/*.json# - num_resources: [250, 500, 750, 1000, 1250, 1500]# - num_steps: [12, 16, 20, 24, 28, 32]# - edge_density: [0.5, 0.4, 0.3, 0.2, 0.1]# - p_val: [0.9, 0.8, 0.6, 0.5, 0.4, 0.2, 0.1]# - threshold: [4.0, 5.0, 6.0, 7.0, 8.0]# This generates 6 × 6 × 5 × 7 × 5 × 2 = 12,600 simulation runs# (2 modes: BAS and ADP)

Custom Parameter Configuration

Modify configuration files in src/assets/config/ to customize simulations:

# Edit basic simulation parameters
vim src/assets/config/basic_config.json
# Edit adaptive model parameters
vim src/assets/config/adaptive_config.json
# Edit network topology parameters
vim src/assets/config/policy_graph_config.json

Analysis Customization

fromsrc.analysis.analysisimportSecurityUsabilityAnalyzer# Initialize analyzeranalyzer=SecurityUsabilityAnalyzer()
# Load custom data filesanalyzer.load_and_merge_data(
bas_file="path/to/custom_bas_data.csv",
adp_file="path/to/custom_adp_data.csv"
)
# Generate specific analysisanalyzer.create_narrative_visualization()

📁 Project Structure

ztSim/
├── README.md # This file - project overview and setup
├── pyproject.toml # Poetry configuration and dependencies
├── src/ # Source code directory
│ ├── README.md # Source code documentation
│ ├── analysis/ # Analysis tools and visualizations
│ │ ├── README.md # Analysis documentation
│ │ ├── analysis.py # Main thesis analysis script
│ │ ├── security_analysis.py # Strategic security analysis script
│ │ ├── analyzer.ipynb # Interactive analysis notebook
│ │ ├── att_analyzer.ipynb # Attack analysis notebook
│ │ └── plots/ # Generated visualizations
│ ├── assets/ # Configuration and data files
│ │ ├── config/ # JSON configuration files
│ │ ├── archive/ # Historical simulation datasets (organized by run number)
│ │ └── results/ # New simulation outputs
│ ├── base_runner.py # Main simulation runner (user behavior)
│ ├── attack_runner.py # Attack simulation runner
│ ├── simulator.py # Core simulation engine
│ ├── attack_simulator.py # Attack sequence simulator
│ ├── user.py # User behavior models
│ ├── authenticator.py # Authentication system
│ ├── policy_generator.py # Network policy graph generator
│ ├── delta.py # Trust factor penalty calculations
│ └── utils.py # Utility functions
└── ...

🔬 Key Results & Findings

Thesis Validation: "ADP is More Secure Without Sacrificing Usability"

Our comprehensive analysis of 12,600 simulation runs demonstrates:

🔒 Security Improvements (ADP vs BAS)

Safe Moves: Significant reduction (attackers have less freedom to move undetected)
Attack Completion: Fewer successful attacks (better threat prevention)
Proactive Defense: ADP stops attacks before completion, while BAS allows all attacks to complete

👤 Usability Preservation Through Strategic Design

Smart Challenge Strategy: ADP uses proactive defense (early termination) rather than reactive challenge escalation
Surgical Precision: Authentication challenges are strategically focused where they matter most
Challenge Efficiency: Fewer total challenges with better security outcomes - demonstrating "smarter security"
User Experience: Enhanced security without challenge fatigue for legitimate users

⚙️ Breaking the Trade-off with Strategic Intelligence

Traditional Assumption: More security = worse usability ❌
ADP Innovation: Strategic security + maintained usability ✅
Key Insight: ADP prevents problematic attacks early rather than burdening users with excessive authentication
Result: Surgically precise security interventions that enhance protection while preserving user productivity

Generated Visualizations (10 Total)

From analysis.py (8 plots):

Safe Moves Analysis: Security improvement across all parameters (nr, ns, ed, p, th)
Attack Completion Rates: Threat prevention effectiveness across all parameters
Auth Challenge Analysis: Usability for completed attacks across all parameters
Legitimate User Study: Usability for high P values (0.8, 0.9) - no degradation for trusted users
Attacker Usability Analysis: Usability for low P values (0.1, 0.2)
All Attacks Usability: Overall usability including terminated attacks
Terminated Attacks Usability: Usability for attacks stopped by ADP
Thesis Narrative Figure: Publication-quality 2×2 summary panel

From security_analysis.py (2 plots): 9. Early Detection Analysis: How quickly ADP identifies and stops threats (moves and challenges before action) 10. Challenge Distribution Analysis: Strategic authentication focus patterns (scatter plots showing challenges vs moves)

🔧 Configuration Parameters

Network Topology (`policy_graph_config.json`)

num_resources: [250, 500, 750, 1000, 1250, 1500] - Network size
num_steps: [12, 16, 20, 24, 28, 32] - Workflow complexity
edge_density: [0.5, 0.4, 0.3, 0.2, 0.1] - Network connectivity

User Behavior (`basic_config.json`)

p_val: [0.9, 0.8, 0.6, 0.5, 0.4, 0.2, 0.1] - Legitimacy probability
max_session_len: 50 - Maximum session duration
history_depth: 3 - User history tracking depth

Adaptive Model (`adaptive_config.json`)

trust_factor_threshold: [4.0, 5.0, 6.0, 7.0, 8.0] - Security thresholds
init_trust_factor: 7.0 - Initial user trust
base_trust_factor: 10.0 - Maximum trust level

Authentication (`auth_config.json`)

num_auth_levels: 4 - Authentication complexity levels
auth_prob_config: Level-specific success probabilities
access_denied_limit: 3 - Maximum failed attempts

🧪 Testing & Validation

Run Analysis Tests

# Test the core analysis script with existing data
python src/analysis/analysis.py
# Test the security analysis script
python src/analysis/security_analysis.py
# Expected output: 10 visualizations + statistical summaries# Check: src/analysis/plots/ for generated files

Validate Simulation

# Quick simulation test (modify config for smaller parameter space)
python src/base_runner.py
# Check: src/assets/results/ for new CSV files

Interactive Exploration

# Launch Jupyter for detailed investigation
jupyter lab src/analysis/analyzer.ipynb

📈 Performance & Scalability

Simulation Scale

Current Dataset: 12,600 simulation runs
Parameter Space: 5-dimensional configuration matrix
Execution Time: ~2-4 hours for full parameter sweep
Memory Usage: ~2-4GB peak during large simulations

Analysis Performance

Data Loading: <10 seconds for 12,600 records
Visualization Generation: ~30-60 seconds for all plots
Statistical Calculations: <5 seconds for thesis metrics

🤝 Contributing

Development Setup

# Install development dependencies
poetry install --with dev
# Run code formatting
poetry run black src/
poetry run isort src/
# Run type checking
poetry run mypy src/

Adding New Analysis

Create analysis functions in src/analysis/
Follow existing patterns for data loading and visualization
Update documentation in src/analysis/ANALYSIS_README.md
Test with existing datasets before committing

📚 Documentation

README.md (this file): Project overview and setup
src/README.md: Source code structure and modules
src/analysis/README.md: Analysis tools, methods, and thesis documentation
- Core Analysis (analysis.py): Parameter analysis, usability studies, narrative visualization
- Strategic Security Analysis (security_analysis.py): Early detection and challenge distribution
Jupyter Notebooks: Interactive analysis examples
- src/analysis/analyzer.ipynb: Custom exploratory analysis
- src/analysis/att_analyzer.ipynb: Attack sequence analysis

🐛 Troubleshooting

Common Issues

Import Errors

# Ensure you're in the Poetry environment
poetry shell
# Reinstall dependencies if needed
poetry install --sync

Missing Data Files

# Check if simulation data exists
ls src/assets/archive/
# Run simulations if data is missing
python src/base_runner.py

Visualization Issues

# Install GUI backend for matplotlib
poetry add tkinter # or appropriate backend# Use non-interactive backend if neededexport MPLBACKEND=Agg

📄 License

This project is part of academic research on zero-trust security architectures. Please cite appropriately if using this work in academic publications.

🎯 Next Steps

Run the analysis: python src/analysis/analysis.py
Explore the data: Open src/analysis/analyzer.ipynb
Review findings: Check generated plots in src/analysis/plots/
Customize parameters: Modify files in src/assets/config/
Generate new data: Run python src/base_runner.py

The simulation demonstrates that enhanced security doesn't require usability sacrifice - ADP proves this empirically across 12,600 test scenarios.

ztSim - Zero Trust Simulation Framework

A comprehensive simulation framework for comparing zero-trust security models, specifically designed to demonstrate that the ADP (Adaptive) model is more secure than the BAS (Baseline) model without sacrificing usability.

🎯 Project Overview

This project implements a sophisticated zero-trust simulation environment that models user behavior, authentication challenges, and security policies in enterprise networks. The simulation compares two zero-trust models:

BAS (Baseline): Traditional zero-trust model with static policies
ADP (Adaptive): Enhanced zero-trust model with dynamic trust factors and adaptive mechanisms

Key Research Question

"Can we enhance security without sacrificing usability in zero-trust architectures?"

The simulation demonstrates that ADP successfully breaks the traditional security-usability trade-off paradigm, providing:

Significant reduction in safe moves (better security - fewer opportunities for attackers)
Fewer completed attacks (better threat prevention)
Improved auth challenge efficiency for completed attacks (maintained usability)
No usability degradation for legitimate users (comparable or improved experience)

🚀 Key Features

Simulation Capabilities

Multi-parameter experimentation across network configurations
Probabilistic user behavior modeling with Bernoulli distributions
Dynamic policy graph generation with configurable resource networks
Adaptive trust factor mechanisms with real-time penalty calculations
Attack sequence simulation for security evaluation
Comprehensive data collection with 20+ metrics per simulation run

Analysis & Visualization

Publication-quality visualizations demonstrating key narrative
Statistical analysis with significance testing
Interactive Jupyter notebooks for exploratory analysis
Automated report generation with key findings
Parameter sensitivity analysis across all configuration dimensions

Configuration Management

JSON-based configuration system for easy parameter modification
Modular architecture supporting different simulation modes
Reproducible experiments with deterministic seeding
Scalable execution across parameter combinations

📋 Prerequisites

System Requirements

Python 3.13+ (required for latest features)
8GB+ RAM (recommended for large-scale simulations)
2GB+ disk space (for simulation data and visualizations)

Dependencies

The project uses Poetry for dependency management with core packages:

pandas (2.3.2+) - Data manipulation and analysis
matplotlib (3.10.5+) - Visualization and plotting
seaborn (0.13.2+) - Statistical visualization
numpy (2.3.2+) - Numerical computing
scipy (1.16.2+) - Scientific computing
networkx (3.5+) - Graph/network analysis
ipykernel (6.30.1+) - Jupyter notebook support

🛠️ Installation

1. Clone the Repository

git clone <repository-url>cd ztSim

2. Install Poetry (if not already installed)

curl -sSL https://install.python-poetry.org | python3 -

3. Install Dependencies

poetry install

4. Activate Virtual Environment

poetry shell

🎮 Quick Start

Run Complete Analysis (Recommended)

# Analyze existing simulation data and generate visualizations
python src/analysis/analysis.py
# Run security analysis (challenge efficiency and proactive defense)
python src/analysis/security_analysis.py

This will:

Load simulation records from the archive directory
Generate publication-quality visualizations demonstrating the narrative
Display interactive plots with plt.show()
Save all outputs to src/analysis/plots/
Print statistical summaries supporting the narrative

Run New Simulations

Standard User Behavior Simulation

# Generate new BAS and ADP simulation data
python src/base_runner.py

Attack Sequence Simulation

# Simulate attack scenarios for security evaluation
python src/attack_runner.py

Interactive Analysis

# Launch Jupyter for custom analysis
jupyter lab src/analysis/analyzer.ipynb
jupyter lab src/analysis/att_analyzer.ipynb

📊 Usage Examples

Basic Simulation Execution

The simulation automatically runs across all parameter combinations defined in configuration files:

# Parameters are loaded from src/assets/config/*.json# - num_resources: [250, 500, 750, 1000, 1250, 1500]# - num_steps: [12, 16, 20, 24, 28, 32]# - edge_density: [0.5, 0.4, 0.3, 0.2, 0.1]# - p_val: [0.9, 0.8, 0.6, 0.5, 0.4, 0.2, 0.1]# - threshold: [4.0, 5.0, 6.0, 7.0, 8.0]# This generates 6 × 6 × 5 × 7 × 5 × 2 = 12,600 simulation runs# (2 modes: BAS and ADP)

Custom Parameter Configuration

Modify configuration files in src/assets/config/ to customize simulations:

# Edit basic simulation parameters
vim src/assets/config/basic_config.json
# Edit adaptive model parameters
vim src/assets/config/adaptive_config.json
# Edit network topology parameters
vim src/assets/config/policy_graph_config.json

Analysis Customization

fromsrc.analysis.analysisimportSecurityUsabilityAnalyzer# Initialize analyzeranalyzer=SecurityUsabilityAnalyzer()
# Load custom data filesanalyzer.load_and_merge_data(
bas_file="path/to/custom_bas_data.csv",
adp_file="path/to/custom_adp_data.csv"
)
# Generate specific analysisanalyzer.create_narrative_visualization()

📁 Project Structure

ztSim/
├── README.md # This file - project overview and setup
├── pyproject.toml # Poetry configuration and dependencies
├── src/ # Source code directory
│ ├── README.md # Source code documentation
│ ├── analysis/ # Analysis tools and visualizations
│ │ ├── README.md # Analysis documentation
│ │ ├── analysis.py # Main thesis analysis script
│ │ ├── security_analysis.py # Strategic security analysis script
│ │ ├── analyzer.ipynb # Interactive analysis notebook
│ │ ├── att_analyzer.ipynb # Attack analysis notebook
│ │ └── plots/ # Generated visualizations
│ ├── assets/ # Configuration and data files
│ │ ├── config/ # JSON configuration files
│ │ ├── archive/ # Historical simulation datasets (organized by run number)
│ │ └── results/ # New simulation outputs
│ ├── base_runner.py # Main simulation runner (user behavior)
│ ├── attack_runner.py # Attack simulation runner
│ ├── simulator.py # Core simulation engine
│ ├── attack_simulator.py # Attack sequence simulator
│ ├── user.py # User behavior models
│ ├── authenticator.py # Authentication system
│ ├── policy_generator.py # Network policy graph generator
│ ├── delta.py # Trust factor penalty calculations
│ └── utils.py # Utility functions
└── ...

🔬 Key Results & Findings

Thesis Validation: "ADP is More Secure Without Sacrificing Usability"

Our comprehensive analysis of 12,600 simulation runs demonstrates:

🔒 Security Improvements (ADP vs BAS)

Safe Moves: Significant reduction (attackers have less freedom to move undetected)
Attack Completion: Fewer successful attacks (better threat prevention)
Proactive Defense: ADP stops attacks before completion, while BAS allows all attacks to complete

👤 Usability Preservation Through Strategic Design

Smart Challenge Strategy: ADP uses proactive defense (early termination) rather than reactive challenge escalation
Surgical Precision: Authentication challenges are strategically focused where they matter most
Challenge Efficiency: Fewer total challenges with better security outcomes - demonstrating "smarter security"
User Experience: Enhanced security without challenge fatigue for legitimate users

⚙️ Breaking the Trade-off with Strategic Intelligence

Traditional Assumption: More security = worse usability ❌
ADP Innovation: Strategic security + maintained usability ✅
Key Insight: ADP prevents problematic attacks early rather than burdening users with excessive authentication
Result: Surgically precise security interventions that enhance protection while preserving user productivity

Generated Visualizations (10 Total)

From analysis.py (8 plots):

Safe Moves Analysis: Security improvement across all parameters (nr, ns, ed, p, th)
Attack Completion Rates: Threat prevention effectiveness across all parameters
Auth Challenge Analysis: Usability for completed attacks across all parameters
Legitimate User Study: Usability for high P values (0.8, 0.9) - no degradation for trusted users
Attacker Usability Analysis: Usability for low P values (0.1, 0.2)
All Attacks Usability: Overall usability including terminated attacks
Terminated Attacks Usability: Usability for attacks stopped by ADP
Thesis Narrative Figure: Publication-quality 2×2 summary panel

From security_analysis.py (2 plots): 9. Early Detection Analysis: How quickly ADP identifies and stops threats (moves and challenges before action) 10. Challenge Distribution Analysis: Strategic authentication focus patterns (scatter plots showing challenges vs moves)

🔧 Configuration Parameters

Network Topology (`policy_graph_config.json`)

num_resources: [250, 500, 750, 1000, 1250, 1500] - Network size
num_steps: [12, 16, 20, 24, 28, 32] - Workflow complexity
edge_density: [0.5, 0.4, 0.3, 0.2, 0.1] - Network connectivity

User Behavior (`basic_config.json`)

p_val: [0.9, 0.8, 0.6, 0.5, 0.4, 0.2, 0.1] - Legitimacy probability
max_session_len: 50 - Maximum session duration
history_depth: 3 - User history tracking depth

Adaptive Model (`adaptive_config.json`)

trust_factor_threshold: [4.0, 5.0, 6.0, 7.0, 8.0] - Security thresholds
init_trust_factor: 7.0 - Initial user trust
base_trust_factor: 10.0 - Maximum trust level

Authentication (`auth_config.json`)

num_auth_levels: 4 - Authentication complexity levels
auth_prob_config: Level-specific success probabilities
access_denied_limit: 3 - Maximum failed attempts

🧪 Testing & Validation

Run Analysis Tests

# Test the core analysis script with existing data
python src/analysis/analysis.py
# Test the security analysis script
python src/analysis/security_analysis.py
# Expected output: 10 visualizations + statistical summaries# Check: src/analysis/plots/ for generated files

Validate Simulation

# Quick simulation test (modify config for smaller parameter space)
python src/base_runner.py
# Check: src/assets/results/ for new CSV files

Interactive Exploration

# Launch Jupyter for detailed investigation
jupyter lab src/analysis/analyzer.ipynb

📈 Performance & Scalability

Simulation Scale

Current Dataset: 12,600 simulation runs
Parameter Space: 5-dimensional configuration matrix
Execution Time: ~2-4 hours for full parameter sweep
Memory Usage: ~2-4GB peak during large simulations

Analysis Performance

Data Loading: <10 seconds for 12,600 records
Visualization Generation: ~30-60 seconds for all plots
Statistical Calculations: <5 seconds for thesis metrics

🤝 Contributing

Development Setup

# Install development dependencies
poetry install --with dev
# Run code formatting
poetry run black src/
poetry run isort src/
# Run type checking
poetry run mypy src/

Adding New Analysis

Create analysis functions in src/analysis/
Follow existing patterns for data loading and visualization
Update documentation in src/analysis/ANALYSIS_README.md
Test with existing datasets before committing

📚 Documentation

README.md (this file): Project overview and setup
src/README.md: Source code structure and modules
src/analysis/README.md: Analysis tools, methods, and thesis documentation
- Core Analysis (analysis.py): Parameter analysis, usability studies, narrative visualization
- Strategic Security Analysis (security_analysis.py): Early detection and challenge distribution
Jupyter Notebooks: Interactive analysis examples
- src/analysis/analyzer.ipynb: Custom exploratory analysis
- src/analysis/att_analyzer.ipynb: Attack sequence analysis

🐛 Troubleshooting

Common Issues

Import Errors

# Ensure you're in the Poetry environment
poetry shell
# Reinstall dependencies if needed
poetry install --sync

Missing Data Files

# Check if simulation data exists
ls src/assets/archive/
# Run simulations if data is missing
python src/base_runner.py

Visualization Issues

# Install GUI backend for matplotlib
poetry add tkinter # or appropriate backend# Use non-interactive backend if neededexport MPLBACKEND=Agg

📄 License

This project is part of academic research on zero-trust security architectures. Please cite appropriately if using this work in academic publications.

🎯 Next Steps

Run the analysis: python src/analysis/analysis.py
Explore the data: Open src/analysis/analyzer.ipynb
Review findings: Check generated plots in src/analysis/plots/
Customize parameters: Modify files in src/assets/config/
Generate new data: Run python src/base_runner.py

The simulation demonstrates that enhanced security doesn't require usability sacrifice - ADP proves this empirically across 12,600 test scenarios.

ztSim - Zero Trust Simulation Framework

A comprehensive simulation framework for comparing zero-trust security models, specifically designed to demonstrate that the ADP (Adaptive) model is more secure than the BAS (Baseline) model without sacrificing usability.

🎯 Project Overview

This project implements a sophisticated zero-trust simulation environment that models user behavior, authentication challenges, and security policies in enterprise networks. The simulation compares two zero-trust models:

BAS (Baseline): Traditional zero-trust model with static policies
ADP (Adaptive): Enhanced zero-trust model with dynamic trust factors and adaptive mechanisms

Key Research Question

"Can we enhance security without sacrificing usability in zero-trust architectures?"

The simulation demonstrates that ADP successfully breaks the traditional security-usability trade-off paradigm, providing:

Significant reduction in safe moves (better security - fewer opportunities for attackers)
Fewer completed attacks (better threat prevention)
Improved auth challenge efficiency for completed attacks (maintained usability)
No usability degradation for legitimate users (comparable or improved experience)

🚀 Key Features

Simulation Capabilities

Multi-parameter experimentation across network configurations
Probabilistic user behavior modeling with Bernoulli distributions
Dynamic policy graph generation with configurable resource networks
Adaptive trust factor mechanisms with real-time penalty calculations
Attack sequence simulation for security evaluation
Comprehensive data collection with 20+ metrics per simulation run

Analysis & Visualization

Publication-quality visualizations demonstrating key narrative
Statistical analysis with significance testing
Interactive Jupyter notebooks for exploratory analysis
Automated report generation with key findings
Parameter sensitivity analysis across all configuration dimensions

Configuration Management

JSON-based configuration system for easy parameter modification
Modular architecture supporting different simulation modes
Reproducible experiments with deterministic seeding
Scalable execution across parameter combinations

📋 Prerequisites

System Requirements

Python 3.13+ (required for latest features)
8GB+ RAM (recommended for large-scale simulations)
2GB+ disk space (for simulation data and visualizations)

Dependencies

The project uses Poetry for dependency management with core packages:

pandas (2.3.2+) - Data manipulation and analysis
matplotlib (3.10.5+) - Visualization and plotting
seaborn (0.13.2+) - Statistical visualization
numpy (2.3.2+) - Numerical computing
scipy (1.16.2+) - Scientific computing
networkx (3.5+) - Graph/network analysis
ipykernel (6.30.1+) - Jupyter notebook support

🛠️ Installation

1. Clone the Repository

git clone <repository-url>cd ztSim

2. Install Poetry (if not already installed)

curl -sSL https://install.python-poetry.org | python3 -

3. Install Dependencies

poetry install

4. Activate Virtual Environment

poetry shell

🎮 Quick Start

Run Complete Analysis (Recommended)

# Analyze existing simulation data and generate visualizations
python src/analysis/analysis.py
# Run security analysis (challenge efficiency and proactive defense)
python src/analysis/security_analysis.py

This will:

Load simulation records from the archive directory
Generate publication-quality visualizations demonstrating the narrative
Display interactive plots with plt.show()
Save all outputs to src/analysis/plots/
Print statistical summaries supporting the narrative

Run New Simulations

Standard User Behavior Simulation

# Generate new BAS and ADP simulation data
python src/base_runner.py

Attack Sequence Simulation

# Simulate attack scenarios for security evaluation
python src/attack_runner.py

Interactive Analysis

# Launch Jupyter for custom analysis
jupyter lab src/analysis/analyzer.ipynb
jupyter lab src/analysis/att_analyzer.ipynb

📊 Usage Examples

Basic Simulation Execution

The simulation automatically runs across all parameter combinations defined in configuration files:

# Parameters are loaded from src/assets/config/*.json# - num_resources: [250, 500, 750, 1000, 1250, 1500]# - num_steps: [12, 16, 20, 24, 28, 32]# - edge_density: [0.5, 0.4, 0.3, 0.2, 0.1]# - p_val: [0.9, 0.8, 0.6, 0.5, 0.4, 0.2, 0.1]# - threshold: [4.0, 5.0, 6.0, 7.0, 8.0]# This generates 6 × 6 × 5 × 7 × 5 × 2 = 12,600 simulation runs# (2 modes: BAS and ADP)

Custom Parameter Configuration

Modify configuration files in src/assets/config/ to customize simulations:

# Edit basic simulation parameters
vim src/assets/config/basic_config.json
# Edit adaptive model parameters
vim src/assets/config/adaptive_config.json
# Edit network topology parameters
vim src/assets/config/policy_graph_config.json

Analysis Customization

fromsrc.analysis.analysisimportSecurityUsabilityAnalyzer# Initialize analyzeranalyzer=SecurityUsabilityAnalyzer()
# Load custom data filesanalyzer.load_and_merge_data(
bas_file="path/to/custom_bas_data.csv",
adp_file="path/to/custom_adp_data.csv"
)
# Generate specific analysisanalyzer.create_narrative_visualization()

📁 Project Structure

ztSim/
├── README.md # This file - project overview and setup
├── pyproject.toml # Poetry configuration and dependencies
├── src/ # Source code directory
│ ├── README.md # Source code documentation
│ ├── analysis/ # Analysis tools and visualizations
│ │ ├── README.md # Analysis documentation
│ │ ├── analysis.py # Main thesis analysis script
│ │ ├── security_analysis.py # Strategic security analysis script
│ │ ├── analyzer.ipynb # Interactive analysis notebook
│ │ ├── att_analyzer.ipynb # Attack analysis notebook
│ │ └── plots/ # Generated visualizations
│ ├── assets/ # Configuration and data files
│ │ ├── config/ # JSON configuration files
│ │ ├── archive/ # Historical simulation datasets (organized by run number)
│ │ └── results/ # New simulation outputs
│ ├── base_runner.py # Main simulation runner (user behavior)
│ ├── attack_runner.py # Attack simulation runner
│ ├── simulator.py # Core simulation engine
│ ├── attack_simulator.py # Attack sequence simulator
│ ├── user.py # User behavior models
│ ├── authenticator.py # Authentication system
│ ├── policy_generator.py # Network policy graph generator
│ ├── delta.py # Trust factor penalty calculations
│ └── utils.py # Utility functions
└── ...

🔬 Key Results & Findings

Thesis Validation: "ADP is More Secure Without Sacrificing Usability"

Our comprehensive analysis of 12,600 simulation runs demonstrates:

🔒 Security Improvements (ADP vs BAS)

Safe Moves: Significant reduction (attackers have less freedom to move undetected)
Attack Completion: Fewer successful attacks (better threat prevention)
Proactive Defense: ADP stops attacks before completion, while BAS allows all attacks to complete

👤 Usability Preservation Through Strategic Design

Smart Challenge Strategy: ADP uses proactive defense (early termination) rather than reactive challenge escalation
Surgical Precision: Authentication challenges are strategically focused where they matter most
Challenge Efficiency: Fewer total challenges with better security outcomes - demonstrating "smarter security"
User Experience: Enhanced security without challenge fatigue for legitimate users

⚙️ Breaking the Trade-off with Strategic Intelligence

Traditional Assumption: More security = worse usability ❌
ADP Innovation: Strategic security + maintained usability ✅
Key Insight: ADP prevents problematic attacks early rather than burdening users with excessive authentication
Result: Surgically precise security interventions that enhance protection while preserving user productivity

Generated Visualizations (10 Total)

From analysis.py (8 plots):

Safe Moves Analysis: Security improvement across all parameters (nr, ns, ed, p, th)
Attack Completion Rates: Threat prevention effectiveness across all parameters
Auth Challenge Analysis: Usability for completed attacks across all parameters
Legitimate User Study: Usability for high P values (0.8, 0.9) - no degradation for trusted users
Attacker Usability Analysis: Usability for low P values (0.1, 0.2)
All Attacks Usability: Overall usability including terminated attacks
Terminated Attacks Usability: Usability for attacks stopped by ADP
Thesis Narrative Figure: Publication-quality 2×2 summary panel

From security_analysis.py (2 plots): 9. Early Detection Analysis: How quickly ADP identifies and stops threats (moves and challenges before action) 10. Challenge Distribution Analysis: Strategic authentication focus patterns (scatter plots showing challenges vs moves)

🔧 Configuration Parameters

Network Topology (`policy_graph_config.json`)

num_resources: [250, 500, 750, 1000, 1250, 1500] - Network size
num_steps: [12, 16, 20, 24, 28, 32] - Workflow complexity
edge_density: [0.5, 0.4, 0.3, 0.2, 0.1] - Network connectivity

User Behavior (`basic_config.json`)

p_val: [0.9, 0.8, 0.6, 0.5, 0.4, 0.2, 0.1] - Legitimacy probability
max_session_len: 50 - Maximum session duration
history_depth: 3 - User history tracking depth

Adaptive Model (`adaptive_config.json`)

trust_factor_threshold: [4.0, 5.0, 6.0, 7.0, 8.0] - Security thresholds
init_trust_factor: 7.0 - Initial user trust
base_trust_factor: 10.0 - Maximum trust level

Authentication (`auth_config.json`)

num_auth_levels: 4 - Authentication complexity levels
auth_prob_config: Level-specific success probabilities
access_denied_limit: 3 - Maximum failed attempts

🧪 Testing & Validation

Run Analysis Tests

# Test the core analysis script with existing data
python src/analysis/analysis.py
# Test the security analysis script
python src/analysis/security_analysis.py
# Expected output: 10 visualizations + statistical summaries# Check: src/analysis/plots/ for generated files

Validate Simulation

# Quick simulation test (modify config for smaller parameter space)
python src/base_runner.py
# Check: src/assets/results/ for new CSV files

Interactive Exploration

# Launch Jupyter for detailed investigation
jupyter lab src/analysis/analyzer.ipynb

📈 Performance & Scalability

Simulation Scale

Current Dataset: 12,600 simulation runs
Parameter Space: 5-dimensional configuration matrix
Execution Time: ~2-4 hours for full parameter sweep
Memory Usage: ~2-4GB peak during large simulations

Analysis Performance

Data Loading: <10 seconds for 12,600 records
Visualization Generation: ~30-60 seconds for all plots
Statistical Calculations: <5 seconds for thesis metrics

🤝 Contributing

Development Setup

# Install development dependencies
poetry install --with dev
# Run code formatting
poetry run black src/
poetry run isort src/
# Run type checking
poetry run mypy src/

Adding New Analysis

Create analysis functions in src/analysis/
Follow existing patterns for data loading and visualization
Update documentation in src/analysis/ANALYSIS_README.md
Test with existing datasets before committing

📚 Documentation

README.md (this file): Project overview and setup
src/README.md: Source code structure and modules
src/analysis/README.md: Analysis tools, methods, and thesis documentation
- Core Analysis (analysis.py): Parameter analysis, usability studies, narrative visualization
- Strategic Security Analysis (security_analysis.py): Early detection and challenge distribution
Jupyter Notebooks: Interactive analysis examples
- src/analysis/analyzer.ipynb: Custom exploratory analysis
- src/analysis/att_analyzer.ipynb: Attack sequence analysis

🐛 Troubleshooting

Common Issues

Import Errors

# Ensure you're in the Poetry environment
poetry shell
# Reinstall dependencies if needed
poetry install --sync

Missing Data Files

# Check if simulation data exists
ls src/assets/archive/
# Run simulations if data is missing
python src/base_runner.py

Visualization Issues

# Install GUI backend for matplotlib
poetry add tkinter # or appropriate backend# Use non-interactive backend if neededexport MPLBACKEND=Agg

📄 License

This project is part of academic research on zero-trust security architectures. Please cite appropriately if using this work in academic publications.

🎯 Next Steps

Run the analysis: python src/analysis/analysis.py
Explore the data: Open src/analysis/analyzer.ipynb
Review findings: Check generated plots in src/analysis/plots/
Customize parameters: Modify files in src/assets/config/
Generate new data: Run python src/base_runner.py

The simulation demonstrates that enhanced security doesn't require usability sacrifice - ADP proves this empirically across 12,600 test scenarios.

ztSim - Zero Trust Simulation Framework

A comprehensive simulation framework for comparing zero-trust security models, specifically designed to demonstrate that the ADP (Adaptive) model is more secure than the BAS (Baseline) model without sacrificing usability.

🎯 Project Overview

This project implements a sophisticated zero-trust simulation environment that models user behavior, authentication challenges, and security policies in enterprise networks. The simulation compares two zero-trust models:

BAS (Baseline): Traditional zero-trust model with static policies
ADP (Adaptive): Enhanced zero-trust model with dynamic trust factors and adaptive mechanisms

Key Research Question

"Can we enhance security without sacrificing usability in zero-trust architectures?"

The simulation demonstrates that ADP successfully breaks the traditional security-usability trade-off paradigm, providing:

Significant reduction in safe moves (better security - fewer opportunities for attackers)
Fewer completed attacks (better threat prevention)
Improved auth challenge efficiency for completed attacks (maintained usability)
No usability degradation for legitimate users (comparable or improved experience)

🚀 Key Features

Simulation Capabilities

Multi-parameter experimentation across network configurations
Probabilistic user behavior modeling with Bernoulli distributions
Dynamic policy graph generation with configurable resource networks
Adaptive trust factor mechanisms with real-time penalty calculations
Attack sequence simulation for security evaluation
Comprehensive data collection with 20+ metrics per simulation run

Analysis & Visualization

Publication-quality visualizations demonstrating key narrative
Statistical analysis with significance testing
Interactive Jupyter notebooks for exploratory analysis
Automated report generation with key findings
Parameter sensitivity analysis across all configuration dimensions

Configuration Management

JSON-based configuration system for easy parameter modification
Modular architecture supporting different simulation modes
Reproducible experiments with deterministic seeding
Scalable execution across parameter combinations

📋 Prerequisites

System Requirements

Python 3.13+ (required for latest features)
8GB+ RAM (recommended for large-scale simulations)
2GB+ disk space (for simulation data and visualizations)

Dependencies

The project uses Poetry for dependency management with core packages:

pandas (2.3.2+) - Data manipulation and analysis
matplotlib (3.10.5+) - Visualization and plotting
seaborn (0.13.2+) - Statistical visualization
numpy (2.3.2+) - Numerical computing
scipy (1.16.2+) - Scientific computing
networkx (3.5+) - Graph/network analysis
ipykernel (6.30.1+) - Jupyter notebook support

🛠️ Installation

1. Clone the Repository

git clone <repository-url>cd ztSim

2. Install Poetry (if not already installed)

curl -sSL https://install.python-poetry.org | python3 -

3. Install Dependencies

poetry install

4. Activate Virtual Environment

poetry shell

🎮 Quick Start

Run Complete Analysis (Recommended)

# Analyze existing simulation data and generate visualizations
python src/analysis/analysis.py
# Run security analysis (challenge efficiency and proactive defense)
python src/analysis/security_analysis.py

This will:

Load simulation records from the archive directory
Generate publication-quality visualizations demonstrating the narrative
Display interactive plots with plt.show()
Save all outputs to src/analysis/plots/
Print statistical summaries supporting the narrative

Run New Simulations

Standard User Behavior Simulation

# Generate new BAS and ADP simulation data
python src/base_runner.py

Attack Sequence Simulation

# Simulate attack scenarios for security evaluation
python src/attack_runner.py

Interactive Analysis

# Launch Jupyter for custom analysis
jupyter lab src/analysis/analyzer.ipynb
jupyter lab src/analysis/att_analyzer.ipynb

📊 Usage Examples

Basic Simulation Execution

The simulation automatically runs across all parameter combinations defined in configuration files:

# Parameters are loaded from src/assets/config/*.json# - num_resources: [250, 500, 750, 1000, 1250, 1500]# - num_steps: [12, 16, 20, 24, 28, 32]# - edge_density: [0.5, 0.4, 0.3, 0.2, 0.1]# - p_val: [0.9, 0.8, 0.6, 0.5, 0.4, 0.2, 0.1]# - threshold: [4.0, 5.0, 6.0, 7.0, 8.0]# This generates 6 × 6 × 5 × 7 × 5 × 2 = 12,600 simulation runs# (2 modes: BAS and ADP)

Custom Parameter Configuration

Modify configuration files in src/assets/config/ to customize simulations:

# Edit basic simulation parameters
vim src/assets/config/basic_config.json
# Edit adaptive model parameters
vim src/assets/config/adaptive_config.json
# Edit network topology parameters
vim src/assets/config/policy_graph_config.json

Analysis Customization

fromsrc.analysis.analysisimportSecurityUsabilityAnalyzer# Initialize analyzeranalyzer=SecurityUsabilityAnalyzer()
# Load custom data filesanalyzer.load_and_merge_data(
bas_file="path/to/custom_bas_data.csv",
adp_file="path/to/custom_adp_data.csv"
)
# Generate specific analysisanalyzer.create_narrative_visualization()

📁 Project Structure

ztSim/
├── README.md # This file - project overview and setup
├── pyproject.toml # Poetry configuration and dependencies
├── src/ # Source code directory
│ ├── README.md # Source code documentation
│ ├── analysis/ # Analysis tools and visualizations
│ │ ├── README.md # Analysis documentation
│ │ ├── analysis.py # Main thesis analysis script
│ │ ├── security_analysis.py # Strategic security analysis script
│ │ ├── analyzer.ipynb # Interactive analysis notebook
│ │ ├── att_analyzer.ipynb # Attack analysis notebook
│ │ └── plots/ # Generated visualizations
│ ├── assets/ # Configuration and data files
│ │ ├── config/ # JSON configuration files
│ │ ├── archive/ # Historical simulation datasets (organized by run number)
│ │ └── results/ # New simulation outputs
│ ├── base_runner.py # Main simulation runner (user behavior)
│ ├── attack_runner.py # Attack simulation runner
│ ├── simulator.py # Core simulation engine
│ ├── attack_simulator.py # Attack sequence simulator
│ ├── user.py # User behavior models
│ ├── authenticator.py # Authentication system
│ ├── policy_generator.py # Network policy graph generator
│ ├── delta.py # Trust factor penalty calculations
│ └── utils.py # Utility functions
└── ...

🔬 Key Results & Findings

Thesis Validation: "ADP is More Secure Without Sacrificing Usability"

Our comprehensive analysis of 12,600 simulation runs demonstrates:

🔒 Security Improvements (ADP vs BAS)

Safe Moves: Significant reduction (attackers have less freedom to move undetected)
Attack Completion: Fewer successful attacks (better threat prevention)
Proactive Defense: ADP stops attacks before completion, while BAS allows all attacks to complete

👤 Usability Preservation Through Strategic Design

Smart Challenge Strategy: ADP uses proactive defense (early termination) rather than reactive challenge escalation
Surgical Precision: Authentication challenges are strategically focused where they matter most
Challenge Efficiency: Fewer total challenges with better security outcomes - demonstrating "smarter security"
User Experience: Enhanced security without challenge fatigue for legitimate users

⚙️ Breaking the Trade-off with Strategic Intelligence

Traditional Assumption: More security = worse usability ❌
ADP Innovation: Strategic security + maintained usability ✅
Key Insight: ADP prevents problematic attacks early rather than burdening users with excessive authentication
Result: Surgically precise security interventions that enhance protection while preserving user productivity

Generated Visualizations (10 Total)

From analysis.py (8 plots):

Safe Moves Analysis: Security improvement across all parameters (nr, ns, ed, p, th)
Attack Completion Rates: Threat prevention effectiveness across all parameters
Auth Challenge Analysis: Usability for completed attacks across all parameters
Legitimate User Study: Usability for high P values (0.8, 0.9) - no degradation for trusted users
Attacker Usability Analysis: Usability for low P values (0.1, 0.2)
All Attacks Usability: Overall usability including terminated attacks
Terminated Attacks Usability: Usability for attacks stopped by ADP
Thesis Narrative Figure: Publication-quality 2×2 summary panel

From security_analysis.py (2 plots): 9. Early Detection Analysis: How quickly ADP identifies and stops threats (moves and challenges before action) 10. Challenge Distribution Analysis: Strategic authentication focus patterns (scatter plots showing challenges vs moves)

🔧 Configuration Parameters

Network Topology (`policy_graph_config.json`)

num_resources: [250, 500, 750, 1000, 1250, 1500] - Network size
num_steps: [12, 16, 20, 24, 28, 32] - Workflow complexity
edge_density: [0.5, 0.4, 0.3, 0.2, 0.1] - Network connectivity

User Behavior (`basic_config.json`)

p_val: [0.9, 0.8, 0.6, 0.5, 0.4, 0.2, 0.1] - Legitimacy probability
max_session_len: 50 - Maximum session duration
history_depth: 3 - User history tracking depth

Adaptive Model (`adaptive_config.json`)

trust_factor_threshold: [4.0, 5.0, 6.0, 7.0, 8.0] - Security thresholds
init_trust_factor: 7.0 - Initial user trust
base_trust_factor: 10.0 - Maximum trust level

Authentication (`auth_config.json`)

num_auth_levels: 4 - Authentication complexity levels
auth_prob_config: Level-specific success probabilities
access_denied_limit: 3 - Maximum failed attempts

🧪 Testing & Validation

Run Analysis Tests

# Test the core analysis script with existing data
python src/analysis/analysis.py
# Test the security analysis script
python src/analysis/security_analysis.py
# Expected output: 10 visualizations + statistical summaries# Check: src/analysis/plots/ for generated files

Validate Simulation

# Quick simulation test (modify config for smaller parameter space)
python src/base_runner.py
# Check: src/assets/results/ for new CSV files

Interactive Exploration

# Launch Jupyter for detailed investigation
jupyter lab src/analysis/analyzer.ipynb

📈 Performance & Scalability

Simulation Scale

Current Dataset: 12,600 simulation runs
Parameter Space: 5-dimensional configuration matrix
Execution Time: ~2-4 hours for full parameter sweep
Memory Usage: ~2-4GB peak during large simulations

Analysis Performance

Data Loading: <10 seconds for 12,600 records
Visualization Generation: ~30-60 seconds for all plots
Statistical Calculations: <5 seconds for thesis metrics

🤝 Contributing

Development Setup

# Install development dependencies
poetry install --with dev
# Run code formatting
poetry run black src/
poetry run isort src/
# Run type checking
poetry run mypy src/

Adding New Analysis

Create analysis functions in src/analysis/
Follow existing patterns for data loading and visualization
Update documentation in src/analysis/ANALYSIS_README.md
Test with existing datasets before committing

📚 Documentation

README.md (this file): Project overview and setup
src/README.md: Source code structure and modules
src/analysis/README.md: Analysis tools, methods, and thesis documentation
- Core Analysis (analysis.py): Parameter analysis, usability studies, narrative visualization
- Strategic Security Analysis (security_analysis.py): Early detection and challenge distribution
Jupyter Notebooks: Interactive analysis examples
- src/analysis/analyzer.ipynb: Custom exploratory analysis
- src/analysis/att_analyzer.ipynb: Attack sequence analysis

🐛 Troubleshooting

Common Issues

Import Errors

# Ensure you're in the Poetry environment
poetry shell
# Reinstall dependencies if needed
poetry install --sync

Missing Data Files

# Check if simulation data exists
ls src/assets/archive/
# Run simulations if data is missing
python src/base_runner.py

Visualization Issues

# Install GUI backend for matplotlib
poetry add tkinter # or appropriate backend# Use non-interactive backend if neededexport MPLBACKEND=Agg

📄 License

This project is part of academic research on zero-trust security architectures. Please cite appropriately if using this work in academic publications.

🎯 Next Steps

Run the analysis: python src/analysis/analysis.py
Explore the data: Open src/analysis/analyzer.ipynb
Review findings: Check generated plots in src/analysis/plots/
Customize parameters: Modify files in src/assets/config/
Generate new data: Run python src/base_runner.py

The simulation demonstrates that enhanced security doesn't require usability sacrifice - ADP proves this empirically across 12,600 test scenarios.

ztSim - Zero Trust Simulation Framework

A comprehensive simulation framework for comparing zero-trust security models, specifically designed to demonstrate that the ADP (Adaptive) model is more secure than the BAS (Baseline) model without sacrificing usability.

🎯 Project Overview

This project implements a sophisticated zero-trust simulation environment that models user behavior, authentication challenges, and security policies in enterprise networks. The simulation compares two zero-trust models:

BAS (Baseline): Traditional zero-trust model with static policies
ADP (Adaptive): Enhanced zero-trust model with dynamic trust factors and adaptive mechanisms

Key Research Question

"Can we enhance security without sacrificing usability in zero-trust architectures?"

The simulation demonstrates that ADP successfully breaks the traditional security-usability trade-off paradigm, providing:

Significant reduction in safe moves (better security - fewer opportunities for attackers)
Fewer completed attacks (better threat prevention)
Improved auth challenge efficiency for completed attacks (maintained usability)
No usability degradation for legitimate users (comparable or improved experience)

🚀 Key Features

Simulation Capabilities

Multi-parameter experimentation across network configurations
Probabilistic user behavior modeling with Bernoulli distributions
Dynamic policy graph generation with configurable resource networks
Adaptive trust factor mechanisms with real-time penalty calculations
Attack sequence simulation for security evaluation
Comprehensive data collection with 20+ metrics per simulation run

Analysis & Visualization

Publication-quality visualizations demonstrating key narrative
Statistical analysis with significance testing
Interactive Jupyter notebooks for exploratory analysis
Automated report generation with key findings
Parameter sensitivity analysis across all configuration dimensions

Configuration Management

JSON-based configuration system for easy parameter modification
Modular architecture supporting different simulation modes
Reproducible experiments with deterministic seeding
Scalable execution across parameter combinations

📋 Prerequisites

System Requirements

Python 3.13+ (required for latest features)
8GB+ RAM (recommended for large-scale simulations)
2GB+ disk space (for simulation data and visualizations)

Dependencies

The project uses Poetry for dependency management with core packages:

pandas (2.3.2+) - Data manipulation and analysis
matplotlib (3.10.5+) - Visualization and plotting
seaborn (0.13.2+) - Statistical visualization
numpy (2.3.2+) - Numerical computing
scipy (1.16.2+) - Scientific computing
networkx (3.5+) - Graph/network analysis
ipykernel (6.30.1+) - Jupyter notebook support

🛠️ Installation

1. Clone the Repository

git clone <repository-url>cd ztSim

2. Install Poetry (if not already installed)

curl -sSL https://install.python-poetry.org | python3 -

3. Install Dependencies

poetry install

4. Activate Virtual Environment

poetry shell

🎮 Quick Start

Run Complete Analysis (Recommended)

# Analyze existing simulation data and generate visualizations
python src/analysis/analysis.py
# Run security analysis (challenge efficiency and proactive defense)
python src/analysis/security_analysis.py

This will:

Load simulation records from the archive directory
Generate publication-quality visualizations demonstrating the narrative
Display interactive plots with plt.show()
Save all outputs to src/analysis/plots/
Print statistical summaries supporting the narrative

Run New Simulations

Standard User Behavior Simulation

# Generate new BAS and ADP simulation data
python src/base_runner.py

Attack Sequence Simulation

# Simulate attack scenarios for security evaluation
python src/attack_runner.py

Interactive Analysis

# Launch Jupyter for custom analysis
jupyter lab src/analysis/analyzer.ipynb
jupyter lab src/analysis/att_analyzer.ipynb

📊 Usage Examples

Basic Simulation Execution

The simulation automatically runs across all parameter combinations defined in configuration files:

# Parameters are loaded from src/assets/config/*.json# - num_resources: [250, 500, 750, 1000, 1250, 1500]# - num_steps: [12, 16, 20, 24, 28, 32]# - edge_density: [0.5, 0.4, 0.3, 0.2, 0.1]# - p_val: [0.9, 0.8, 0.6, 0.5, 0.4, 0.2, 0.1]# - threshold: [4.0, 5.0, 6.0, 7.0, 8.0]# This generates 6 × 6 × 5 × 7 × 5 × 2 = 12,600 simulation runs# (2 modes: BAS and ADP)

Custom Parameter Configuration

Modify configuration files in src/assets/config/ to customize simulations:

# Edit basic simulation parameters
vim src/assets/config/basic_config.json
# Edit adaptive model parameters
vim src/assets/config/adaptive_config.json
# Edit network topology parameters
vim src/assets/config/policy_graph_config.json

Analysis Customization

fromsrc.analysis.analysisimportSecurityUsabilityAnalyzer# Initialize analyzeranalyzer=SecurityUsabilityAnalyzer()
# Load custom data filesanalyzer.load_and_merge_data(
bas_file="path/to/custom_bas_data.csv",
adp_file="path/to/custom_adp_data.csv"
)
# Generate specific analysisanalyzer.create_narrative_visualization()

📁 Project Structure

ztSim/
├── README.md # This file - project overview and setup
├── pyproject.toml # Poetry configuration and dependencies
├── src/ # Source code directory
│ ├── README.md # Source code documentation
│ ├── analysis/ # Analysis tools and visualizations
│ │ ├── README.md # Analysis documentation
│ │ ├── analysis.py # Main thesis analysis script
│ │ ├── security_analysis.py # Strategic security analysis script
│ │ ├── analyzer.ipynb # Interactive analysis notebook
│ │ ├── att_analyzer.ipynb # Attack analysis notebook
│ │ └── plots/ # Generated visualizations
│ ├── assets/ # Configuration and data files
│ │ ├── config/ # JSON configuration files
│ │ ├── archive/ # Historical simulation datasets (organized by run number)
│ │ └── results/ # New simulation outputs
│ ├── base_runner.py # Main simulation runner (user behavior)
│ ├── attack_runner.py # Attack simulation runner
│ ├── simulator.py # Core simulation engine
│ ├── attack_simulator.py # Attack sequence simulator
│ ├── user.py # User behavior models
│ ├── authenticator.py # Authentication system
│ ├── policy_generator.py # Network policy graph generator
│ ├── delta.py # Trust factor penalty calculations
│ └── utils.py # Utility functions
└── ...

🔬 Key Results & Findings

Thesis Validation: "ADP is More Secure Without Sacrificing Usability"

Our comprehensive analysis of 12,600 simulation runs demonstrates:

🔒 Security Improvements (ADP vs BAS)

Safe Moves: Significant reduction (attackers have less freedom to move undetected)
Attack Completion: Fewer successful attacks (better threat prevention)
Proactive Defense: ADP stops attacks before completion, while BAS allows all attacks to complete

👤 Usability Preservation Through Strategic Design

Smart Challenge Strategy: ADP uses proactive defense (early termination) rather than reactive challenge escalation
Surgical Precision: Authentication challenges are strategically focused where they matter most
Challenge Efficiency: Fewer total challenges with better security outcomes - demonstrating "smarter security"
User Experience: Enhanced security without challenge fatigue for legitimate users

⚙️ Breaking the Trade-off with Strategic Intelligence

Traditional Assumption: More security = worse usability ❌
ADP Innovation: Strategic security + maintained usability ✅
Key Insight: ADP prevents problematic attacks early rather than burdening users with excessive authentication
Result: Surgically precise security interventions that enhance protection while preserving user productivity

Generated Visualizations (10 Total)

From analysis.py (8 plots):

Safe Moves Analysis: Security improvement across all parameters (nr, ns, ed, p, th)
Attack Completion Rates: Threat prevention effectiveness across all parameters
Auth Challenge Analysis: Usability for completed attacks across all parameters
Legitimate User Study: Usability for high P values (0.8, 0.9) - no degradation for trusted users
Attacker Usability Analysis: Usability for low P values (0.1, 0.2)
All Attacks Usability: Overall usability including terminated attacks
Terminated Attacks Usability: Usability for attacks stopped by ADP
Thesis Narrative Figure: Publication-quality 2×2 summary panel

From security_analysis.py (2 plots): 9. Early Detection Analysis: How quickly ADP identifies and stops threats (moves and challenges before action) 10. Challenge Distribution Analysis: Strategic authentication focus patterns (scatter plots showing challenges vs moves)

🔧 Configuration Parameters

Network Topology (`policy_graph_config.json`)

num_resources: [250, 500, 750, 1000, 1250, 1500] - Network size
num_steps: [12, 16, 20, 24, 28, 32] - Workflow complexity
edge_density: [0.5, 0.4, 0.3, 0.2, 0.1] - Network connectivity

User Behavior (`basic_config.json`)

p_val: [0.9, 0.8, 0.6, 0.5, 0.4, 0.2, 0.1] - Legitimacy probability
max_session_len: 50 - Maximum session duration
history_depth: 3 - User history tracking depth

Adaptive Model (`adaptive_config.json`)

trust_factor_threshold: [4.0, 5.0, 6.0, 7.0, 8.0] - Security thresholds
init_trust_factor: 7.0 - Initial user trust
base_trust_factor: 10.0 - Maximum trust level

Authentication (`auth_config.json`)

num_auth_levels: 4 - Authentication complexity levels
auth_prob_config: Level-specific success probabilities
access_denied_limit: 3 - Maximum failed attempts

🧪 Testing & Validation

Run Analysis Tests

# Test the core analysis script with existing data
python src/analysis/analysis.py
# Test the security analysis script
python src/analysis/security_analysis.py
# Expected output: 10 visualizations + statistical summaries# Check: src/analysis/plots/ for generated files

Validate Simulation

# Quick simulation test (modify config for smaller parameter space)
python src/base_runner.py
# Check: src/assets/results/ for new CSV files

Interactive Exploration

# Launch Jupyter for detailed investigation
jupyter lab src/analysis/analyzer.ipynb

📈 Performance & Scalability

Simulation Scale

Current Dataset: 12,600 simulation runs
Parameter Space: 5-dimensional configuration matrix
Execution Time: ~2-4 hours for full parameter sweep
Memory Usage: ~2-4GB peak during large simulations

Analysis Performance

Data Loading: <10 seconds for 12,600 records
Visualization Generation: ~30-60 seconds for all plots
Statistical Calculations: <5 seconds for thesis metrics

🤝 Contributing

Development Setup

# Install development dependencies
poetry install --with dev
# Run code formatting
poetry run black src/
poetry run isort src/
# Run type checking
poetry run mypy src/

Adding New Analysis

Create analysis functions in src/analysis/
Follow existing patterns for data loading and visualization
Update documentation in src/analysis/ANALYSIS_README.md
Test with existing datasets before committing

📚 Documentation

README.md (this file): Project overview and setup
src/README.md: Source code structure and modules
src/analysis/README.md: Analysis tools, methods, and thesis documentation
- Core Analysis (analysis.py): Parameter analysis, usability studies, narrative visualization
- Strategic Security Analysis (security_analysis.py): Early detection and challenge distribution
Jupyter Notebooks: Interactive analysis examples
- src/analysis/analyzer.ipynb: Custom exploratory analysis
- src/analysis/att_analyzer.ipynb: Attack sequence analysis

🐛 Troubleshooting

Common Issues

Import Errors

# Ensure you're in the Poetry environment
poetry shell
# Reinstall dependencies if needed
poetry install --sync

Missing Data Files

# Check if simulation data exists
ls src/assets/archive/
# Run simulations if data is missing
python src/base_runner.py

Visualization Issues

# Install GUI backend for matplotlib
poetry add tkinter # or appropriate backend# Use non-interactive backend if neededexport MPLBACKEND=Agg

📄 License

This project is part of academic research on zero-trust security architectures. Please cite appropriately if using this work in academic publications.

🎯 Next Steps

Run the analysis: python src/analysis/analysis.py
Explore the data: Open src/analysis/analyzer.ipynb
Review findings: Check generated plots in src/analysis/plots/
Customize parameters: Modify files in src/assets/config/
Generate new data: Run python src/base_runner.py

The simulation demonstrates that enhanced security doesn't require usability sacrifice - ADP proves this empirically across 12,600 test scenarios.

ztSim - Zero Trust Simulation Framework

A comprehensive simulation framework for comparing zero-trust security models, specifically designed to demonstrate that the ADP (Adaptive) model is more secure than the BAS (Baseline) model without sacrificing usability.

🎯 Project Overview

This project implements a sophisticated zero-trust simulation environment that models user behavior, authentication challenges, and security policies in enterprise networks. The simulation compares two zero-trust models:

BAS (Baseline): Traditional zero-trust model with static policies
ADP (Adaptive): Enhanced zero-trust model with dynamic trust factors and adaptive mechanisms

Key Research Question

"Can we enhance security without sacrificing usability in zero-trust architectures?"

The simulation demonstrates that ADP successfully breaks the traditional security-usability trade-off paradigm, providing:

Significant reduction in safe moves (better security - fewer opportunities for attackers)
Fewer completed attacks (better threat prevention)
Improved auth challenge efficiency for completed attacks (maintained usability)
No usability degradation for legitimate users (comparable or improved experience)

🚀 Key Features

Simulation Capabilities

Multi-parameter experimentation across network configurations
Probabilistic user behavior modeling with Bernoulli distributions
Dynamic policy graph generation with configurable resource networks
Adaptive trust factor mechanisms with real-time penalty calculations
Attack sequence simulation for security evaluation
Comprehensive data collection with 20+ metrics per simulation run

Analysis & Visualization

Publication-quality visualizations demonstrating key narrative
Statistical analysis with significance testing
Interactive Jupyter notebooks for exploratory analysis
Automated report generation with key findings
Parameter sensitivity analysis across all configuration dimensions

Configuration Management

JSON-based configuration system for easy parameter modification
Modular architecture supporting different simulation modes
Reproducible experiments with deterministic seeding
Scalable execution across parameter combinations

📋 Prerequisites

System Requirements

Python 3.13+ (required for latest features)
8GB+ RAM (recommended for large-scale simulations)
2GB+ disk space (for simulation data and visualizations)

Dependencies

The project uses Poetry for dependency management with core packages:

pandas (2.3.2+) - Data manipulation and analysis
matplotlib (3.10.5+) - Visualization and plotting
seaborn (0.13.2+) - Statistical visualization
numpy (2.3.2+) - Numerical computing
scipy (1.16.2+) - Scientific computing
networkx (3.5+) - Graph/network analysis
ipykernel (6.30.1+) - Jupyter notebook support

🛠️ Installation

1. Clone the Repository

git clone <repository-url>cd ztSim

2. Install Poetry (if not already installed)

curl -sSL https://install.python-poetry.org | python3 -

3. Install Dependencies

poetry install

4. Activate Virtual Environment

poetry shell

🎮 Quick Start

Run Complete Analysis (Recommended)

# Analyze existing simulation data and generate visualizations
python src/analysis/analysis.py
# Run security analysis (challenge efficiency and proactive defense)
python src/analysis/security_analysis.py

This will:

Load simulation records from the archive directory
Generate publication-quality visualizations demonstrating the narrative
Display interactive plots with plt.show()
Save all outputs to src/analysis/plots/
Print statistical summaries supporting the narrative

Run New Simulations

Standard User Behavior Simulation

# Generate new BAS and ADP simulation data
python src/base_runner.py

Attack Sequence Simulation

# Simulate attack scenarios for security evaluation
python src/attack_runner.py

Interactive Analysis

# Launch Jupyter for custom analysis
jupyter lab src/analysis/analyzer.ipynb
jupyter lab src/analysis/att_analyzer.ipynb

📊 Usage Examples

Basic Simulation Execution

The simulation automatically runs across all parameter combinations defined in configuration files:

# Parameters are loaded from src/assets/config/*.json# - num_resources: [250, 500, 750, 1000, 1250, 1500]# - num_steps: [12, 16, 20, 24, 28, 32]# - edge_density: [0.5, 0.4, 0.3, 0.2, 0.1]# - p_val: [0.9, 0.8, 0.6, 0.5, 0.4, 0.2, 0.1]# - threshold: [4.0, 5.0, 6.0, 7.0, 8.0]# This generates 6 × 6 × 5 × 7 × 5 × 2 = 12,600 simulation runs# (2 modes: BAS and ADP)

Custom Parameter Configuration

Modify configuration files in src/assets/config/ to customize simulations:

# Edit basic simulation parameters
vim src/assets/config/basic_config.json
# Edit adaptive model parameters
vim src/assets/config/adaptive_config.json
# Edit network topology parameters
vim src/assets/config/policy_graph_config.json

Analysis Customization

fromsrc.analysis.analysisimportSecurityUsabilityAnalyzer# Initialize analyzeranalyzer=SecurityUsabilityAnalyzer()
# Load custom data filesanalyzer.load_and_merge_data(
bas_file="path/to/custom_bas_data.csv",
adp_file="path/to/custom_adp_data.csv"
)
# Generate specific analysisanalyzer.create_narrative_visualization()

📁 Project Structure

ztSim/
├── README.md # This file - project overview and setup
├── pyproject.toml # Poetry configuration and dependencies
├── src/ # Source code directory
│ ├── README.md # Source code documentation
│ ├── analysis/ # Analysis tools and visualizations
│ │ ├── README.md # Analysis documentation
│ │ ├── analysis.py # Main thesis analysis script
│ │ ├── security_analysis.py # Strategic security analysis script
│ │ ├── analyzer.ipynb # Interactive analysis notebook
│ │ ├── att_analyzer.ipynb # Attack analysis notebook
│ │ └── plots/ # Generated visualizations
│ ├── assets/ # Configuration and data files
│ │ ├── config/ # JSON configuration files
│ │ ├── archive/ # Historical simulation datasets (organized by run number)
│ │ └── results/ # New simulation outputs
│ ├── base_runner.py # Main simulation runner (user behavior)
│ ├── attack_runner.py # Attack simulation runner
│ ├── simulator.py # Core simulation engine
│ ├── attack_simulator.py # Attack sequence simulator
│ ├── user.py # User behavior models
│ ├── authenticator.py # Authentication system
│ ├── policy_generator.py # Network policy graph generator
│ ├── delta.py # Trust factor penalty calculations
│ └── utils.py # Utility functions
└── ...

🔬 Key Results & Findings

Thesis Validation: "ADP is More Secure Without Sacrificing Usability"

Our comprehensive analysis of 12,600 simulation runs demonstrates:

🔒 Security Improvements (ADP vs BAS)

Safe Moves: Significant reduction (attackers have less freedom to move undetected)
Attack Completion: Fewer successful attacks (better threat prevention)
Proactive Defense: ADP stops attacks before completion, while BAS allows all attacks to complete

👤 Usability Preservation Through Strategic Design

Smart Challenge Strategy: ADP uses proactive defense (early termination) rather than reactive challenge escalation
Surgical Precision: Authentication challenges are strategically focused where they matter most
Challenge Efficiency: Fewer total challenges with better security outcomes - demonstrating "smarter security"
User Experience: Enhanced security without challenge fatigue for legitimate users

⚙️ Breaking the Trade-off with Strategic Intelligence

Traditional Assumption: More security = worse usability ❌
ADP Innovation: Strategic security + maintained usability ✅
Key Insight: ADP prevents problematic attacks early rather than burdening users with excessive authentication
Result: Surgically precise security interventions that enhance protection while preserving user productivity

Generated Visualizations (10 Total)

From analysis.py (8 plots):

Safe Moves Analysis: Security improvement across all parameters (nr, ns, ed, p, th)
Attack Completion Rates: Threat prevention effectiveness across all parameters
Auth Challenge Analysis: Usability for completed attacks across all parameters
Legitimate User Study: Usability for high P values (0.8, 0.9) - no degradation for trusted users
Attacker Usability Analysis: Usability for low P values (0.1, 0.2)
All Attacks Usability: Overall usability including terminated attacks
Terminated Attacks Usability: Usability for attacks stopped by ADP
Thesis Narrative Figure: Publication-quality 2×2 summary panel

From security_analysis.py (2 plots): 9. Early Detection Analysis: How quickly ADP identifies and stops threats (moves and challenges before action) 10. Challenge Distribution Analysis: Strategic authentication focus patterns (scatter plots showing challenges vs moves)

🔧 Configuration Parameters

Network Topology (`policy_graph_config.json`)

num_resources: [250, 500, 750, 1000, 1250, 1500] - Network size
num_steps: [12, 16, 20, 24, 28, 32] - Workflow complexity
edge_density: [0.5, 0.4, 0.3, 0.2, 0.1] - Network connectivity

User Behavior (`basic_config.json`)

p_val: [0.9, 0.8, 0.6, 0.5, 0.4, 0.2, 0.1] - Legitimacy probability
max_session_len: 50 - Maximum session duration
history_depth: 3 - User history tracking depth

Adaptive Model (`adaptive_config.json`)

trust_factor_threshold: [4.0, 5.0, 6.0, 7.0, 8.0] - Security thresholds
init_trust_factor: 7.0 - Initial user trust
base_trust_factor: 10.0 - Maximum trust level

Authentication (`auth_config.json`)

num_auth_levels: 4 - Authentication complexity levels
auth_prob_config: Level-specific success probabilities
access_denied_limit: 3 - Maximum failed attempts

🧪 Testing & Validation

Run Analysis Tests

# Test the core analysis script with existing data
python src/analysis/analysis.py
# Test the security analysis script
python src/analysis/security_analysis.py
# Expected output: 10 visualizations + statistical summaries# Check: src/analysis/plots/ for generated files

Validate Simulation

# Quick simulation test (modify config for smaller parameter space)
python src/base_runner.py
# Check: src/assets/results/ for new CSV files

Interactive Exploration

# Launch Jupyter for detailed investigation
jupyter lab src/analysis/analyzer.ipynb

📈 Performance & Scalability

Simulation Scale

Current Dataset: 12,600 simulation runs
Parameter Space: 5-dimensional configuration matrix
Execution Time: ~2-4 hours for full parameter sweep
Memory Usage: ~2-4GB peak during large simulations

Analysis Performance

Data Loading: <10 seconds for 12,600 records
Visualization Generation: ~30-60 seconds for all plots
Statistical Calculations: <5 seconds for thesis metrics

🤝 Contributing

Development Setup

# Install development dependencies
poetry install --with dev
# Run code formatting
poetry run black src/
poetry run isort src/
# Run type checking
poetry run mypy src/

Adding New Analysis

Create analysis functions in src/analysis/
Follow existing patterns for data loading and visualization
Update documentation in src/analysis/ANALYSIS_README.md
Test with existing datasets before committing

📚 Documentation

README.md (this file): Project overview and setup
src/README.md: Source code structure and modules
src/analysis/README.md: Analysis tools, methods, and thesis documentation
- Core Analysis (analysis.py): Parameter analysis, usability studies, narrative visualization
- Strategic Security Analysis (security_analysis.py): Early detection and challenge distribution
Jupyter Notebooks: Interactive analysis examples
- src/analysis/analyzer.ipynb: Custom exploratory analysis
- src/analysis/att_analyzer.ipynb: Attack sequence analysis

🐛 Troubleshooting

Common Issues

Import Errors

# Ensure you're in the Poetry environment
poetry shell
# Reinstall dependencies if needed
poetry install --sync

Missing Data Files

# Check if simulation data exists
ls src/assets/archive/
# Run simulations if data is missing
python src/base_runner.py

Visualization Issues

# Install GUI backend for matplotlib
poetry add tkinter # or appropriate backend# Use non-interactive backend if neededexport MPLBACKEND=Agg

📄 License

This project is part of academic research on zero-trust security architectures. Please cite appropriately if using this work in academic publications.

🎯 Next Steps

Run the analysis: python src/analysis/analysis.py
Explore the data: Open src/analysis/analyzer.ipynb
Review findings: Check generated plots in src/analysis/plots/
Customize parameters: Modify files in src/assets/config/
Generate new data: Run python src/base_runner.py

The simulation demonstrates that enhanced security doesn't require usability sacrifice - ADP proves this empirically across 12,600 test scenarios.

ztSim - Zero Trust Simulation Framework

A comprehensive simulation framework for comparing zero-trust security models, specifically designed to demonstrate that the ADP (Adaptive) model is more secure than the BAS (Baseline) model without sacrificing usability.

🎯 Project Overview

This project implements a sophisticated zero-trust simulation environment that models user behavior, authentication challenges, and security policies in enterprise networks. The simulation compares two zero-trust models:

BAS (Baseline): Traditional zero-trust model with static policies
ADP (Adaptive): Enhanced zero-trust model with dynamic trust factors and adaptive mechanisms

Key Research Question

"Can we enhance security without sacrificing usability in zero-trust architectures?"

The simulation demonstrates that ADP successfully breaks the traditional security-usability trade-off paradigm, providing:

Significant reduction in safe moves (better security - fewer opportunities for attackers)
Fewer completed attacks (better threat prevention)
Improved auth challenge efficiency for completed attacks (maintained usability)
No usability degradation for legitimate users (comparable or improved experience)

🚀 Key Features

Simulation Capabilities

Multi-parameter experimentation across network configurations
Probabilistic user behavior modeling with Bernoulli distributions
Dynamic policy graph generation with configurable resource networks
Adaptive trust factor mechanisms with real-time penalty calculations
Attack sequence simulation for security evaluation
Comprehensive data collection with 20+ metrics per simulation run

Analysis & Visualization

Publication-quality visualizations demonstrating key narrative
Statistical analysis with significance testing
Interactive Jupyter notebooks for exploratory analysis
Automated report generation with key findings
Parameter sensitivity analysis across all configuration dimensions

Configuration Management

JSON-based configuration system for easy parameter modification
Modular architecture supporting different simulation modes
Reproducible experiments with deterministic seeding
Scalable execution across parameter combinations

📋 Prerequisites

System Requirements

Python 3.13+ (required for latest features)
8GB+ RAM (recommended for large-scale simulations)
2GB+ disk space (for simulation data and visualizations)

Dependencies

The project uses Poetry for dependency management with core packages:

pandas (2.3.2+) - Data manipulation and analysis
matplotlib (3.10.5+) - Visualization and plotting
seaborn (0.13.2+) - Statistical visualization
numpy (2.3.2+) - Numerical computing
scipy (1.16.2+) - Scientific computing
networkx (3.5+) - Graph/network analysis
ipykernel (6.30.1+) - Jupyter notebook support

🛠️ Installation

1. Clone the Repository

git clone <repository-url>cd ztSim

2. Install Poetry (if not already installed)

curl -sSL https://install.python-poetry.org | python3 -

3. Install Dependencies

poetry install

4. Activate Virtual Environment

poetry shell

🎮 Quick Start

Run Complete Analysis (Recommended)

# Analyze existing simulation data and generate visualizations
python src/analysis/analysis.py
# Run security analysis (challenge efficiency and proactive defense)
python src/analysis/security_analysis.py

This will:

Load simulation records from the archive directory
Generate publication-quality visualizations demonstrating the narrative
Display interactive plots with plt.show()
Save all outputs to src/analysis/plots/
Print statistical summaries supporting the narrative

Run New Simulations

Standard User Behavior Simulation

# Generate new BAS and ADP simulation data
python src/base_runner.py

Attack Sequence Simulation

# Simulate attack scenarios for security evaluation
python src/attack_runner.py

Interactive Analysis

# Launch Jupyter for custom analysis
jupyter lab src/analysis/analyzer.ipynb
jupyter lab src/analysis/att_analyzer.ipynb

📊 Usage Examples

Basic Simulation Execution

The simulation automatically runs across all parameter combinations defined in configuration files:

# Parameters are loaded from src/assets/config/*.json# - num_resources: [250, 500, 750, 1000, 1250, 1500]# - num_steps: [12, 16, 20, 24, 28, 32]# - edge_density: [0.5, 0.4, 0.3, 0.2, 0.1]# - p_val: [0.9, 0.8, 0.6, 0.5, 0.4, 0.2, 0.1]# - threshold: [4.0, 5.0, 6.0, 7.0, 8.0]# This generates 6 × 6 × 5 × 7 × 5 × 2 = 12,600 simulation runs# (2 modes: BAS and ADP)

Custom Parameter Configuration

Modify configuration files in src/assets/config/ to customize simulations:

# Edit basic simulation parameters
vim src/assets/config/basic_config.json
# Edit adaptive model parameters
vim src/assets/config/adaptive_config.json
# Edit network topology parameters
vim src/assets/config/policy_graph_config.json

Analysis Customization

fromsrc.analysis.analysisimportSecurityUsabilityAnalyzer# Initialize analyzeranalyzer=SecurityUsabilityAnalyzer()
# Load custom data filesanalyzer.load_and_merge_data(
bas_file="path/to/custom_bas_data.csv",
adp_file="path/to/custom_adp_data.csv"
)
# Generate specific analysisanalyzer.create_narrative_visualization()

📁 Project Structure

ztSim/
├── README.md # This file - project overview and setup
├── pyproject.toml # Poetry configuration and dependencies
├── src/ # Source code directory
│ ├── README.md # Source code documentation
│ ├── analysis/ # Analysis tools and visualizations
│ │ ├── README.md # Analysis documentation
│ │ ├── analysis.py # Main thesis analysis script
│ │ ├── security_analysis.py # Strategic security analysis script
│ │ ├── analyzer.ipynb # Interactive analysis notebook
│ │ ├── att_analyzer.ipynb # Attack analysis notebook
│ │ └── plots/ # Generated visualizations
│ ├── assets/ # Configuration and data files
│ │ ├── config/ # JSON configuration files
│ │ ├── archive/ # Historical simulation datasets (organized by run number)
│ │ └── results/ # New simulation outputs
│ ├── base_runner.py # Main simulation runner (user behavior)
│ ├── attack_runner.py # Attack simulation runner
│ ├── simulator.py # Core simulation engine
│ ├── attack_simulator.py # Attack sequence simulator
│ ├── user.py # User behavior models
│ ├── authenticator.py # Authentication system
│ ├── policy_generator.py # Network policy graph generator
│ ├── delta.py # Trust factor penalty calculations
│ └── utils.py # Utility functions
└── ...

🔬 Key Results & Findings

Thesis Validation: "ADP is More Secure Without Sacrificing Usability"

Our comprehensive analysis of 12,600 simulation runs demonstrates:

🔒 Security Improvements (ADP vs BAS)

Safe Moves: Significant reduction (attackers have less freedom to move undetected)
Attack Completion: Fewer successful attacks (better threat prevention)
Proactive Defense: ADP stops attacks before completion, while BAS allows all attacks to complete

👤 Usability Preservation Through Strategic Design

Smart Challenge Strategy: ADP uses proactive defense (early termination) rather than reactive challenge escalation
Surgical Precision: Authentication challenges are strategically focused where they matter most
Challenge Efficiency: Fewer total challenges with better security outcomes - demonstrating "smarter security"
User Experience: Enhanced security without challenge fatigue for legitimate users

⚙️ Breaking the Trade-off with Strategic Intelligence

Traditional Assumption: More security = worse usability ❌
ADP Innovation: Strategic security + maintained usability ✅
Key Insight: ADP prevents problematic attacks early rather than burdening users with excessive authentication
Result: Surgically precise security interventions that enhance protection while preserving user productivity

Generated Visualizations (10 Total)

From analysis.py (8 plots):

Safe Moves Analysis: Security improvement across all parameters (nr, ns, ed, p, th)
Attack Completion Rates: Threat prevention effectiveness across all parameters
Auth Challenge Analysis: Usability for completed attacks across all parameters
Legitimate User Study: Usability for high P values (0.8, 0.9) - no degradation for trusted users
Attacker Usability Analysis: Usability for low P values (0.1, 0.2)
All Attacks Usability: Overall usability including terminated attacks
Terminated Attacks Usability: Usability for attacks stopped by ADP
Thesis Narrative Figure: Publication-quality 2×2 summary panel

From security_analysis.py (2 plots): 9. Early Detection Analysis: How quickly ADP identifies and stops threats (moves and challenges before action) 10. Challenge Distribution Analysis: Strategic authentication focus patterns (scatter plots showing challenges vs moves)

🔧 Configuration Parameters

Network Topology (`policy_graph_config.json`)

num_resources: [250, 500, 750, 1000, 1250, 1500] - Network size
num_steps: [12, 16, 20, 24, 28, 32] - Workflow complexity
edge_density: [0.5, 0.4, 0.3, 0.2, 0.1] - Network connectivity

User Behavior (`basic_config.json`)

p_val: [0.9, 0.8, 0.6, 0.5, 0.4, 0.2, 0.1] - Legitimacy probability
max_session_len: 50 - Maximum session duration
history_depth: 3 - User history tracking depth

Adaptive Model (`adaptive_config.json`)

trust_factor_threshold: [4.0, 5.0, 6.0, 7.0, 8.0] - Security thresholds
init_trust_factor: 7.0 - Initial user trust
base_trust_factor: 10.0 - Maximum trust level

Authentication (`auth_config.json`)

num_auth_levels: 4 - Authentication complexity levels
auth_prob_config: Level-specific success probabilities
access_denied_limit: 3 - Maximum failed attempts

🧪 Testing & Validation

Run Analysis Tests

# Test the core analysis script with existing data
python src/analysis/analysis.py
# Test the security analysis script
python src/analysis/security_analysis.py
# Expected output: 10 visualizations + statistical summaries# Check: src/analysis/plots/ for generated files

Validate Simulation

# Quick simulation test (modify config for smaller parameter space)
python src/base_runner.py
# Check: src/assets/results/ for new CSV files

Interactive Exploration

# Launch Jupyter for detailed investigation
jupyter lab src/analysis/analyzer.ipynb

📈 Performance & Scalability

Simulation Scale

Current Dataset: 12,600 simulation runs
Parameter Space: 5-dimensional configuration matrix
Execution Time: ~2-4 hours for full parameter sweep
Memory Usage: ~2-4GB peak during large simulations

Analysis Performance

Data Loading: <10 seconds for 12,600 records
Visualization Generation: ~30-60 seconds for all plots
Statistical Calculations: <5 seconds for thesis metrics

🤝 Contributing

Development Setup

# Install development dependencies
poetry install --with dev
# Run code formatting
poetry run black src/
poetry run isort src/
# Run type checking
poetry run mypy src/

Adding New Analysis

Create analysis functions in src/analysis/
Follow existing patterns for data loading and visualization
Update documentation in src/analysis/ANALYSIS_README.md
Test with existing datasets before committing

📚 Documentation

README.md (this file): Project overview and setup
src/README.md: Source code structure and modules
src/analysis/README.md: Analysis tools, methods, and thesis documentation
- Core Analysis (analysis.py): Parameter analysis, usability studies, narrative visualization
- Strategic Security Analysis (security_analysis.py): Early detection and challenge distribution
Jupyter Notebooks: Interactive analysis examples
- src/analysis/analyzer.ipynb: Custom exploratory analysis
- src/analysis/att_analyzer.ipynb: Attack sequence analysis

🐛 Troubleshooting

Common Issues

Import Errors

# Ensure you're in the Poetry environment
poetry shell
# Reinstall dependencies if needed
poetry install --sync

Missing Data Files

# Check if simulation data exists
ls src/assets/archive/
# Run simulations if data is missing
python src/base_runner.py

Visualization Issues

# Install GUI backend for matplotlib
poetry add tkinter # or appropriate backend# Use non-interactive backend if neededexport MPLBACKEND=Agg

📄 License

This project is part of academic research on zero-trust security architectures. Please cite appropriately if using this work in academic publications.

🎯 Next Steps

Run the analysis: python src/analysis/analysis.py
Explore the data: Open src/analysis/analyzer.ipynb
Review findings: Check generated plots in src/analysis/plots/
Customize parameters: Modify files in src/assets/config/
Generate new data: Run python src/base_runner.py

The simulation demonstrates that enhanced security doesn't require usability sacrifice - ADP proves this empirically across 12,600 test scenarios.

Name		Name	Last commit message	Last commit date
.vscode		.vscode
src		src
tests		tests
.DS_Store		.DS_Store
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
poetry.lock		poetry.lock
pyproject.toml		pyproject.toml

Name		Name	Last commit message	Last commit date
.vscode		.vscode
src		src
tests		tests
.DS_Store		.DS_Store
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
poetry.lock		poetry.lock
pyproject.toml		pyproject.toml

Name		Name	Last commit message	Last commit date
.vscode		.vscode
src		src
tests		tests
.DS_Store		.DS_Store
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
poetry.lock		poetry.lock
pyproject.toml		pyproject.toml

Name		Name	Last commit message	Last commit date
.vscode		.vscode
src		src
tests		tests
.DS_Store		.DS_Store
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
poetry.lock		poetry.lock
pyproject.toml		pyproject.toml

Name		Name	Last commit message	Last commit date
.vscode		.vscode
src		src
tests		tests
.DS_Store		.DS_Store
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
poetry.lock		poetry.lock
pyproject.toml		pyproject.toml

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ztSim - Zero Trust Simulation Framework

🎯 Project Overview

Key Research Question

🚀 Key Features

Simulation Capabilities

Analysis & Visualization

Configuration Management

📋 Prerequisites

System Requirements

Dependencies

🛠️ Installation

1. Clone the Repository

2. Install Poetry (if not already installed)

3. Install Dependencies

4. Activate Virtual Environment

🎮 Quick Start

Run Complete Analysis (Recommended)

Run New Simulations

Standard User Behavior Simulation

Attack Sequence Simulation

Interactive Analysis

📊 Usage Examples

Basic Simulation Execution

Custom Parameter Configuration

Analysis Customization

📁 Project Structure

🔬 Key Results & Findings

Thesis Validation: "ADP is More Secure Without Sacrificing Usability"

🔒 Security Improvements (ADP vs BAS)

👤 Usability Preservation Through Strategic Design

⚙️ Breaking the Trade-off with Strategic Intelligence

Generated Visualizations (10 Total)

🔧 Configuration Parameters

Network Topology (policy_graph_config.json)

User Behavior (basic_config.json)

Adaptive Model (adaptive_config.json)

Authentication (auth_config.json)

🧪 Testing & Validation

Run Analysis Tests

Validate Simulation

Interactive Exploration

📈 Performance & Scalability

Simulation Scale

Analysis Performance

🤝 Contributing

Development Setup

Adding New Analysis

📚 Documentation

🐛 Troubleshooting

Common Issues

Import Errors

Missing Data Files

Visualization Issues

📄 License

🎯 Next Steps

About

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ztSim - Zero Trust Simulation Framework

🎯 Project Overview

Key Research Question

🚀 Key Features

Simulation Capabilities

Analysis & Visualization

Configuration Management

Network Topology (`policy_graph_config.json`)

User Behavior (`basic_config.json`)

Adaptive Model (`adaptive_config.json`)

Authentication (`auth_config.json`)

Network Topology (`policy_graph_config.json`)

User Behavior (`basic_config.json`)

Adaptive Model (`adaptive_config.json`)

Authentication (`auth_config.json`)