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Security Incident Investigation Database

A SQL-based security incident analysis system for investigating security events. Analyzes login attempts, detects suspicious patterns, and retrieves targeted employee data. Uses advanced SQL queries with pattern matching, date filtering, and correlation analysis for efficient incident response.

Features

  • Advanced SQL Queries - Comprehensive queries for security incident analysis
  • Login Attempt Analysis - Detect brute force and suspicious login patterns
  • Pattern Detection - Identify anomalous behavior patterns
  • Automated Reporting - Generate incident reports from query results

Database Schema

The project includes SQL scripts for creating the necessary database schema for security incident tracking.

Requirements

  • Oracle Database 12c+ (or compatible SQL database)
  • SQL*Plus or compatible SQL client
  • Python 3.8+ (for reporting scripts)

Installation

  1. Create the database schema:
sqlplus username/password@database @schema.sql
  1. Load sample data (optional):
sqlplus username/password@database @sample_data.sql

Usage

Analyze Login Attempts

sqlplus username/password@database @queries/login_analysis.sql

Detect Suspicious Patterns

sqlplus username/password@database @queries/suspicious_patterns.sql

Generate Incident Report

python generate_report.py --incident-id 12345

Query Examples

Find Failed Login Attempts

SELECT username, COUNT(*) as failed_attempts, MAX(login_time) as last_attempt
FROM login_logs
WHERE status ='FAILED'AND login_time >SYSDATE-1GROUP BY username
HAVINGCOUNT(*) >5ORDER BY failed_attempts DESC;

Detect Brute Force Attacks

SELECT ip_address, COUNT(DISTINCT username) as unique_users,
COUNT(*) as total_attempts
FROM login_logs
WHERE status ='FAILED'AND login_time >SYSDATE-1GROUP BY ip_address
HAVINGCOUNT(*) >10ORDER BY total_attempts DESC;

Project Structure

security-incident-database/
├── schema.sql # Database schema creation
├── sample_data.sql # Sample data for testing
├── queries/
│ ├── login_analysis.sql # Login attempt queries
│ ├── suspicious_patterns.sql # Pattern detection
│ └── incident_response.sql # Incident response queries
├── reports/
│ └── generate_report.py # Python reporting script
├── README.md # This file
└── .gitignore

Security Notice

⚠️This database may contain sensitive security information. Ensure proper access controls and encryption are in place.

License

MIT License

Author

John Bustamante - Cybersecurity Professional

About

No description, website, or topics provided.

Resources

Readme
MIT license
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

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try {
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Security Incident Investigation Database
A SQL-based security incident analysis system for investigating security events. Analyzes login attempts, detects suspicious patterns, and retrieves targeted employee data. Uses advanced SQL queries with pattern matching, date filtering, and correlation analysis for efficient incident response.
Features
  • Advanced SQL Queries - Comprehensive queries for security incident analysis
  • Login Attempt Analysis - Detect brute force and suspicious login patterns
  • Pattern Detection - Identify anomalous behavior patterns
  • Automated Reporting - Generate incident reports from query results
Database Schema
The project includes SQL scripts for creating the necessary database schema for security incident tracking.
Requirements
  • Oracle Database 12c+ (or compatible SQL database)
  • SQL*Plus or compatible SQL client
  • Python 3.8+ (for reporting scripts)
Installation
  1. Create the database schema:
sqlplus username/password@database @schema.sql
  1. Load sample data (optional):
sqlplus username/password@database @sample_data.sql
Usage
Analyze Login Attempts
sqlplus username/password@database @queries/login_analysis.sql
Detect Suspicious Patterns
sqlplus username/password@database @queries/suspicious_patterns.sql
Generate Incident Report
python generate_report.py --incident-id 12345
Query Examples
Find Failed Login Attempts
SELECT username, COUNT(*) as failed_attempts, MAX(login_time) as last_attempt
FROM login_logs
WHERE status ='FAILED'AND login_time >SYSDATE-1GROUP BY username
HAVINGCOUNT(*) >5ORDER BY failed_attempts DESC;
Detect Brute Force Attacks
SELECT ip_address, COUNT(DISTINCT username) as unique_users,
COUNT(*) as total_attempts
FROM login_logs
WHERE status ='FAILED'AND login_time >SYSDATE-1GROUP BY ip_address
HAVINGCOUNT(*) >10ORDER BY total_attempts DESC;
Project Structure
security-incident-database/
├── schema.sql # Database schema creation
├── sample_data.sql # Sample data for testing
├── queries/
│ ├── login_analysis.sql # Login attempt queries
│ ├── suspicious_patterns.sql # Pattern detection
│ └── incident_response.sql # Incident response queries
├── reports/
│ └── generate_report.py # Python reporting script
├── README.md # This file
└── .gitignore

Security Notice
⚠️This database may contain sensitive security information. Ensure proper access controls and encryption are in place.
License
MIT License
Author
John Bustamante - Cybersecurity Professional
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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try {
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Security Incident Investigation Database
A SQL-based security incident analysis system for investigating security events. Analyzes login attempts, detects suspicious patterns, and retrieves targeted employee data. Uses advanced SQL queries with pattern matching, date filtering, and correlation analysis for efficient incident response.
Features
  • Advanced SQL Queries - Comprehensive queries for security incident analysis
  • Login Attempt Analysis - Detect brute force and suspicious login patterns
  • Pattern Detection - Identify anomalous behavior patterns
  • Automated Reporting - Generate incident reports from query results
Database Schema
The project includes SQL scripts for creating the necessary database schema for security incident tracking.
Requirements
  • Oracle Database 12c+ (or compatible SQL database)
  • SQL*Plus or compatible SQL client
  • Python 3.8+ (for reporting scripts)
Installation
  1. Create the database schema:
sqlplus username/password@database @schema.sql
  1. Load sample data (optional):
sqlplus username/password@database @sample_data.sql
Usage
Analyze Login Attempts
sqlplus username/password@database @queries/login_analysis.sql
Detect Suspicious Patterns
sqlplus username/password@database @queries/suspicious_patterns.sql
Generate Incident Report
python generate_report.py --incident-id 12345
Query Examples
Find Failed Login Attempts
SELECT username, COUNT(*) as failed_attempts, MAX(login_time) as last_attempt
FROM login_logs
WHERE status ='FAILED'AND login_time >SYSDATE-1GROUP BY username
HAVINGCOUNT(*) >5ORDER BY failed_attempts DESC;
Detect Brute Force Attacks
SELECT ip_address, COUNT(DISTINCT username) as unique_users,
COUNT(*) as total_attempts
FROM login_logs
WHERE status ='FAILED'AND login_time >SYSDATE-1GROUP BY ip_address
HAVINGCOUNT(*) >10ORDER BY total_attempts DESC;
Project Structure
security-incident-database/
├── schema.sql # Database schema creation
├── sample_data.sql # Sample data for testing
├── queries/
│ ├── login_analysis.sql # Login attempt queries
│ ├── suspicious_patterns.sql # Pattern detection
│ └── incident_response.sql # Incident response queries
├── reports/
│ └── generate_report.py # Python reporting script
├── README.md # This file
└── .gitignore

Security Notice
⚠️This database may contain sensitive security information. Ensure proper access controls and encryption are in place.
License
MIT License
Author
John Bustamante - Cybersecurity Professional
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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})();
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try {
var __m = "*";
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Security Incident Investigation Database
A SQL-based security incident analysis system for investigating security events. Analyzes login attempts, detects suspicious patterns, and retrieves targeted employee data. Uses advanced SQL queries with pattern matching, date filtering, and correlation analysis for efficient incident response.
Features
  • Advanced SQL Queries - Comprehensive queries for security incident analysis
  • Login Attempt Analysis - Detect brute force and suspicious login patterns
  • Pattern Detection - Identify anomalous behavior patterns
  • Automated Reporting - Generate incident reports from query results
Database Schema
The project includes SQL scripts for creating the necessary database schema for security incident tracking.
Requirements
  • Oracle Database 12c+ (or compatible SQL database)
  • SQL*Plus or compatible SQL client
  • Python 3.8+ (for reporting scripts)
Installation
  1. Create the database schema:
sqlplus username/password@database @schema.sql
  1. Load sample data (optional):
sqlplus username/password@database @sample_data.sql
Usage
Analyze Login Attempts
sqlplus username/password@database @queries/login_analysis.sql
Detect Suspicious Patterns
sqlplus username/password@database @queries/suspicious_patterns.sql
Generate Incident Report
python generate_report.py --incident-id 12345
Query Examples
Find Failed Login Attempts
SELECT username, COUNT(*) as failed_attempts, MAX(login_time) as last_attempt
FROM login_logs
WHERE status ='FAILED'AND login_time >SYSDATE-1GROUP BY username
HAVINGCOUNT(*) >5ORDER BY failed_attempts DESC;
Detect Brute Force Attacks
SELECT ip_address, COUNT(DISTINCT username) as unique_users,
COUNT(*) as total_attempts
FROM login_logs
WHERE status ='FAILED'AND login_time >SYSDATE-1GROUP BY ip_address
HAVINGCOUNT(*) >10ORDER BY total_attempts DESC;
Project Structure
security-incident-database/
├── schema.sql # Database schema creation
├── sample_data.sql # Sample data for testing
├── queries/
│ ├── login_analysis.sql # Login attempt queries
│ ├── suspicious_patterns.sql # Pattern detection
│ └── incident_response.sql # Incident response queries
├── reports/
│ └── generate_report.py # Python reporting script
├── README.md # This file
└── .gitignore

Security Notice
⚠️This database may contain sensitive security information. Ensure proper access controls and encryption are in place.
License
MIT License
Author
John Bustamante - Cybersecurity Professional
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
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})();
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var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Security Incident Investigation Database
A SQL-based security incident analysis system for investigating security events. Analyzes login attempts, detects suspicious patterns, and retrieves targeted employee data. Uses advanced SQL queries with pattern matching, date filtering, and correlation analysis for efficient incident response.
Features
  • Advanced SQL Queries - Comprehensive queries for security incident analysis
  • Login Attempt Analysis - Detect brute force and suspicious login patterns
  • Pattern Detection - Identify anomalous behavior patterns
  • Automated Reporting - Generate incident reports from query results
Database Schema
The project includes SQL scripts for creating the necessary database schema for security incident tracking.
Requirements
  • Oracle Database 12c+ (or compatible SQL database)
  • SQL*Plus or compatible SQL client
  • Python 3.8+ (for reporting scripts)
Installation
  1. Create the database schema:
sqlplus username/password@database @schema.sql
  1. Load sample data (optional):
sqlplus username/password@database @sample_data.sql
Usage
Analyze Login Attempts
sqlplus username/password@database @queries/login_analysis.sql
Detect Suspicious Patterns
sqlplus username/password@database @queries/suspicious_patterns.sql
Generate Incident Report
python generate_report.py --incident-id 12345
Query Examples
Find Failed Login Attempts
SELECT username, COUNT(*) as failed_attempts, MAX(login_time) as last_attempt
FROM login_logs
WHERE status ='FAILED'AND login_time >SYSDATE-1GROUP BY username
HAVINGCOUNT(*) >5ORDER BY failed_attempts DESC;
Detect Brute Force Attacks
SELECT ip_address, COUNT(DISTINCT username) as unique_users,
COUNT(*) as total_attempts
FROM login_logs
WHERE status ='FAILED'AND login_time >SYSDATE-1GROUP BY ip_address
HAVINGCOUNT(*) >10ORDER BY total_attempts DESC;
Project Structure
security-incident-database/
├── schema.sql # Database schema creation
├── sample_data.sql # Sample data for testing
├── queries/
│ ├── login_analysis.sql # Login attempt queries
│ ├── suspicious_patterns.sql # Pattern detection
│ └── incident_response.sql # Incident response queries
├── reports/
│ └── generate_report.py # Python reporting script
├── README.md # This file
└── .gitignore

Security Notice
⚠️This database may contain sensitive security information. Ensure proper access controls and encryption are in place.
License
MIT License
Author
John Bustamante - Cybersecurity Professional
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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if (__m === '*' || __re.test(location.href)) {
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Security Incident Investigation Database
A SQL-based security incident analysis system for investigating security events. Analyzes login attempts, detects suspicious patterns, and retrieves targeted employee data. Uses advanced SQL queries with pattern matching, date filtering, and correlation analysis for efficient incident response.
Features
  • Advanced SQL Queries - Comprehensive queries for security incident analysis
  • Login Attempt Analysis - Detect brute force and suspicious login patterns
  • Pattern Detection - Identify anomalous behavior patterns
  • Automated Reporting - Generate incident reports from query results
Database Schema
The project includes SQL scripts for creating the necessary database schema for security incident tracking.
Requirements
  • Oracle Database 12c+ (or compatible SQL database)
  • SQL*Plus or compatible SQL client
  • Python 3.8+ (for reporting scripts)
Installation
  1. Create the database schema:
sqlplus username/password@database @schema.sql
  1. Load sample data (optional):
sqlplus username/password@database @sample_data.sql
Usage
Analyze Login Attempts
sqlplus username/password@database @queries/login_analysis.sql
Detect Suspicious Patterns
sqlplus username/password@database @queries/suspicious_patterns.sql
Generate Incident Report
python generate_report.py --incident-id 12345
Query Examples
Find Failed Login Attempts
SELECT username, COUNT(*) as failed_attempts, MAX(login_time) as last_attempt
FROM login_logs
WHERE status ='FAILED'AND login_time >SYSDATE-1GROUP BY username
HAVINGCOUNT(*) >5ORDER BY failed_attempts DESC;
Detect Brute Force Attacks
SELECT ip_address, COUNT(DISTINCT username) as unique_users,
COUNT(*) as total_attempts
FROM login_logs
WHERE status ='FAILED'AND login_time >SYSDATE-1GROUP BY ip_address
HAVINGCOUNT(*) >10ORDER BY total_attempts DESC;
Project Structure
security-incident-database/
├── schema.sql # Database schema creation
├── sample_data.sql # Sample data for testing
├── queries/
│ ├── login_analysis.sql # Login attempt queries
│ ├── suspicious_patterns.sql # Pattern detection
│ └── incident_response.sql # Incident response queries
├── reports/
│ └── generate_report.py # Python reporting script
├── README.md # This file
└── .gitignore

Security Notice
⚠️This database may contain sensitive security information. Ensure proper access controls and encryption are in place.
License
MIT License
Author
John Bustamante - Cybersecurity Professional
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

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if (__m === '*' || __re.test(location.href)) {
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try {
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Security Incident Investigation Database
A SQL-based security incident analysis system for investigating security events. Analyzes login attempts, detects suspicious patterns, and retrieves targeted employee data. Uses advanced SQL queries with pattern matching, date filtering, and correlation analysis for efficient incident response.
Features
  • Advanced SQL Queries - Comprehensive queries for security incident analysis
  • Login Attempt Analysis - Detect brute force and suspicious login patterns
  • Pattern Detection - Identify anomalous behavior patterns
  • Automated Reporting - Generate incident reports from query results
Database Schema
The project includes SQL scripts for creating the necessary database schema for security incident tracking.
Requirements
  • Oracle Database 12c+ (or compatible SQL database)
  • SQL*Plus or compatible SQL client
  • Python 3.8+ (for reporting scripts)
Installation
  1. Create the database schema:
sqlplus username/password@database @schema.sql
  1. Load sample data (optional):
sqlplus username/password@database @sample_data.sql
Usage
Analyze Login Attempts
sqlplus username/password@database @queries/login_analysis.sql
Detect Suspicious Patterns
sqlplus username/password@database @queries/suspicious_patterns.sql
Generate Incident Report
python generate_report.py --incident-id 12345
Query Examples
Find Failed Login Attempts
SELECT username, COUNT(*) as failed_attempts, MAX(login_time) as last_attempt
FROM login_logs
WHERE status ='FAILED'AND login_time >SYSDATE-1GROUP BY username
HAVINGCOUNT(*) >5ORDER BY failed_attempts DESC;
Detect Brute Force Attacks
SELECT ip_address, COUNT(DISTINCT username) as unique_users,
COUNT(*) as total_attempts
FROM login_logs
WHERE status ='FAILED'AND login_time >SYSDATE-1GROUP BY ip_address
HAVINGCOUNT(*) >10ORDER BY total_attempts DESC;
Project Structure
security-incident-database/
├── schema.sql # Database schema creation
├── sample_data.sql # Sample data for testing
├── queries/
│ ├── login_analysis.sql # Login attempt queries
│ ├── suspicious_patterns.sql # Pattern detection
│ └── incident_response.sql # Incident response queries
├── reports/
│ └── generate_report.py # Python reporting script
├── README.md # This file
└── .gitignore

Security Notice
⚠️This database may contain sensitive security information. Ensure proper access controls and encryption are in place.
License
MIT License
Author
John Bustamante - Cybersecurity Professional
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
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background: #1a1a2e !important;
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filter: invert(1) hue-rotate(180deg) !important;
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[data-theme="light"], [data-theme="light"],
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filter: none !important;
}
/* Fix common UI elements */
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filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
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observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Security Incident Investigation Database
A SQL-based security incident analysis system for investigating security events. Analyzes login attempts, detects suspicious patterns, and retrieves targeted employee data. Uses advanced SQL queries with pattern matching, date filtering, and correlation analysis for efficient incident response.
Features
  • Advanced SQL Queries - Comprehensive queries for security incident analysis
  • Login Attempt Analysis - Detect brute force and suspicious login patterns
  • Pattern Detection - Identify anomalous behavior patterns
  • Automated Reporting - Generate incident reports from query results
Database Schema
The project includes SQL scripts for creating the necessary database schema for security incident tracking.
Requirements
  • Oracle Database 12c+ (or compatible SQL database)
  • SQL*Plus or compatible SQL client
  • Python 3.8+ (for reporting scripts)
Installation
  1. Create the database schema:
sqlplus username/password@database @schema.sql
  1. Load sample data (optional):
sqlplus username/password@database @sample_data.sql
Usage
Analyze Login Attempts
sqlplus username/password@database @queries/login_analysis.sql
Detect Suspicious Patterns
sqlplus username/password@database @queries/suspicious_patterns.sql
Generate Incident Report
python generate_report.py --incident-id 12345
Query Examples
Find Failed Login Attempts
SELECT username, COUNT(*) as failed_attempts, MAX(login_time) as last_attempt
FROM login_logs
WHERE status ='FAILED'AND login_time >SYSDATE-1GROUP BY username
HAVINGCOUNT(*) >5ORDER BY failed_attempts DESC;
Detect Brute Force Attacks
SELECT ip_address, COUNT(DISTINCT username) as unique_users,
COUNT(*) as total_attempts
FROM login_logs
WHERE status ='FAILED'AND login_time >SYSDATE-1GROUP BY ip_address
HAVINGCOUNT(*) >10ORDER BY total_attempts DESC;
Project Structure
security-incident-database/
├── schema.sql # Database schema creation
├── sample_data.sql # Sample data for testing
├── queries/
│ ├── login_analysis.sql # Login attempt queries
│ ├── suspicious_patterns.sql # Pattern detection
│ └── incident_response.sql # Incident response queries
├── reports/
│ └── generate_report.py # Python reporting script
├── README.md # This file
└── .gitignore

Security Notice
⚠️This database may contain sensitive security information. Ensure proper access controls and encryption are in place.
License
MIT License
Author
John Bustamante - Cybersecurity Professional
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