Full production line for receiving, db storing, visualizing Telemetry form network node, and running Machine Learning on the data for near-realtime Anomaly Detection.

End-to-End Real-time Network Intrusion Detection System using Apache Kafka, PySpark Structured Streaming, and Deep Learning (Autoencoder) on CIC-IDS2017 dataset.

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A real-time anomaly detection system for network traffic using machine learning and a custom Tkinter-based user interface. Trained on the CIC-IDS-2017 dataset, it identifies suspicious patterns using selected network features with the help of random forest classifier along with Scapy for live packet sniffing.

Real-time, GPU-accelerated Layer-5 pre-filter for HTTP traffic. Cuts deep-inspection load by 100–1000× while targeting zero false negatives for SQLi/XSS. Built with FastAPI and RAPIDS/cuML (optional PyTorch), Dockerized; 5–10 Gb/s now with a path to ~50 Gb/s on NVIDIA L40s.

Bu proje, gerçek zamanlı ağ trafiğini dinleyerek olası anomali ve saldırı girişimlerini tespit etmeyi amaçlar.

A low cost and scalable project designed using raspberry pi to find real time anomalies in a wifi network between multiple devices connected to the network

⚡ Realtime anomaly detection dashboard for networks. Built with SvelteKit, FastAPI, Docker, and Nginx.

AMD Vault is a realtime cybersecurity system detecting CPU/Disk anomalies using Isolation Forest AI. On threat detection, it triggers auto mitigation (network isolation, Fernet AES encryption) and secure rapid data recovery! Tech: Python, Scikit-Learn, Node.js, HTML Tailwind Team: Rajat Tripathi (AI/Architecture) & Sneha Singh (Full Stack Dev).

Lightweight network monitoring demo for Windows: sniffs Wi-Fi traffic and shows live logs, alerts, and a traffic chart. Python (Flask + Scapy) + SQLite backend, HTML/CSS/JS + Chart.js frontend with SSE for realtime updates. Simple, rate-based anomaly detection with one-click Export to Excel; ignores loopback, focuses on your Wi-Fi adapter.