This project develops a reconstruction-based anomaly detection system for network intrusions, utilizing autoencoders with attention mechanism to identify intrusions by analyzing the loss in reconstructing network traffic data. This approach works better than classical classifiers, which need to be trained on every possible attack type, and fail to detect novel attacks. Attention mechanism also allows it to capture time dependencies across packets.
Image credit: IEEE