Towards an Unsupervised Reward Function for a Deep Reinforcement Learning Based Intrusion Detection System
Bilel Saghrouchni, Frédéric Le Mouël, Bogdan Szanto · 2024
Intrusion detection systems (IDS) based on deep learning have proven successful, but struggle to learn continuously and detect new attacks over time due to a supervised label-based reward function. In this article, we introduce an unsupervised Deep Double Q Learning (DDQL) method that aims to detect attacks and learn new behaviors through an unsupervised reward function leveraging a normality score inspired by car traffic anomaly detection.