[ICANN 2023] Anomaly-Based Insider Threat Detection via Hierarchical Information Fusion
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Updated
Nov 20, 2023 - Jupyter Notebook
[ICANN 2023] Anomaly-Based Insider Threat Detection via Hierarchical Information Fusion
A comprehensive machine learning and deep learning pipeline for detecting insider threats using the CERT r4.1 dataset. This project combines unsupervised anomaly detection, supervised machine learning, and advanced deep learning architectures to identify anomalous user behavior in enterprise environments.
An end-to-end AI system for detecting insider threats using a hybrid machine learning approach (Isolation Forest + XGBoost). Features a high-performance ETL pipeline using DuckDB, real-time inference via FastAPI, and integrated Explainable AI (SHAP) for transparent risk assessment on the CERT R4.2 dataset.
Patent-aligned cybersecurity prototype implementing dynamic trust-based adaptive access control using credential integrity, competence evidence, behavioral risk, and event-driven trust recomputation.
Cyber - Eye (Frontend only) , hosted via netlify
Insider Threat Monitor
Production-ready insider threat detection with 99.81% accuracy - 5 ML/DL models + XAI (SHAP/LIME)
FedShield-ID: Privacy-First Identity Trust Platform using Federated Learning, Differential Privacy, Post-Quantum Security, Explainable AI and Behavioral Analytics for Banking Networks.
A custom MITRE ATT&CK®-style framework for insider threat detection and investigation — structured as a kill chain, mapped to ATT&CK, with 130+ detection ID mappings.
AI-Powered Multi-Agent SOC (Security Operations Center) Platform A cybersecurity pipeline using 5 AI agents for real-time insider threat detection, risk analysis, and automated response. Built with Django, Next.js, Llama 3.1, LangGraph, and Reinforcement Learning. Developed as part of the 4th-year Integrated Project at Esprit School of Engineering
As part of my Cybersecurity Honours Module (COS 720) at UP, I had to develop a lightweight AI powered prototype system that detects potential insider threats in a corporate environment by analysing user behavioural features from organisational logs.
Enterprise-grade continuous trust intelligence and insider threat detection platform powered by Post-Quantum Cryptography (PQC) and AI.
Two-stage insider threat detection on CERT r4.2: lightweight XGBoost screening + LLM-based reasoning over graph-informed narratives. University of Ottawa, CSI 5388.
Network profiling and behavior analysis
Experimental code for the PhD dissertation research on data leak detection in corporate networks based on evolutionary algorithms
*This simulation captures core, widely observed attacker behaviors aligned with common enterprise intrusion patterns. From brute-force access to obfuscated execution, persistence, recon, and privilege assessment, each step reflects actions that threat actors commonly execute after compromising a host.
Cyber Projects
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