CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-Level
Fraud detection system using machine learning and deep learning (XGBoost + Autoencoder). Trains on synthetic financial transactions to flag suspicious activity with business-focused evaluation metrics.
This repository hosts a comprehensive, web-based Anomaly Detection platform designed to identify irregularities across three distinct domains: Network Security, Industrial Manufacturing, and Medical Imaging. Built with Flask REST API and powered by PyTorch and Scikit-learn.
Unsupervised anomaly detection for network traffic using Isolation Forest. Captures live packets, extracts statistical features, and flags threats in real-time via a React dashboard.
University Assignment, it includes 3 different projects, about anomaly detection, kernel regression and stochastic text generation
End-to-end real-time anomaly detection system leveraging Kafka streaming and stateful behavioural features for low-latency anomaly detection.
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