Real-time eBPF-powered network security monitor with AI-driven threat detection. Surfaces port scans, DDoS attacks, botnet activity, and anomalies at 100Gbps+ speeds with sub-microsecond latency (~150 million packets/sec).
This repository is dedicated to our source code for our research paper titled Synthetic Malware Image Generation Based on Generative Models Against Zero-Day Attacks. We presented our research work at the Silicon Valley Cybersecurity Conference 2025.
Hybrid AI-powered Intrusion Detection System (NIDS) combining 1D-CNN & Variational Autoencoder (VAE) to detect known cyberattacks and zero-day anomalies. Features a premium Streamlit "Command Center" dashboard for real-time network traffic analysis.
Behaviour-First Zero-Day Detector (BFZDD) An AI-powered malware detector that learns normal program behaviour using LSTM/GRU/Transformer autoencoders and flags anomalies in real time — enabling true zero-day detection beyond signatures. Includes live trace analysis, fine-tuning UI, model versioning, and event heatmaps.
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