Dataset for Training and Evaluating LLM-Based SOC Agents
-
Updated
Jun 4, 2026 - Python
Dataset for Training and Evaluating LLM-Based SOC Agents
An interactive, chat-driven learning system and production reference architecture for recommendation-system engineering with layered LLM guardrails, built with FastAPI, LangGraph, Ollama, and pgvector, fronted by a modern Next.js / React 19 App Router lab UI.
Security-first AI experiments focused on SOC automation, adversary simulation, forensics, and governance, with an emphasis on safety, reasoning, and real operational use cases.
Decision-aware AI surveillance system with YOLOv8 weapon detection, contextual risk scoring, temporal validation, evidence capture, and automated SMS alerting.
DDoS-Vision: A Deep Learning approach to network security. Converts numerical CIC-IDS2017 traffic flows into 2D grayscale images for CNN classification. Features Grad-CAM for Explainable AI (XAI) and 99% detection accuracy.
GUARDIUM is an intelligent Wazuh rule optimization framework designed to reduce false positives, improve alert accuracy, and assist SOC teams in maintaining high-quality SIEM detections. GUARDIUM combines rule analysis, threat context, and Large Language Models (LLMs) to automatically evaluate, explain, and optimize Wazuh rules.
AI-powered smart surveillance system using YOLOv8, Kalman Filter tracking, and dynamic auto-zoom for real-time object detection and monitoring.
Cybersecurity URL and email phishing detection classifier utilizing lexical and host-based ML features.
Uncertainty-aware trust verification framework integrating Raft-based consensus, policy-driven filtering, and QBM forensic validation. The system targets robust detection of adversarial AIS behaviors, explicitly modeling gray-zone decisions and trade-offs between security, efficiency, and reliability in maritime blockchain networks.
To associate your repository with the security-ai topic, visit your repo's landing page and select "manage topics."