Search-based test and improvement of machine-learning-based anomaly detection systems

Machine-learning-based anomaly detection systems can be vulnerable to new kinds of deceptions, known as training attacks, which exploit the live learning mechanism of these systems by progressively injecting small portions of abnormal data. The injected data seamlessly swift the learned states to a point where harmful data can pass unnoticed. We focus on the systematic testing of these attacks in the context of intrusion detection systems (IDS). We propose a search-based approach to test IDS by making training attacks. Going a step further, we also propose searching for countermeasures, learning from the successful attacks and thereby increasing the resilience of the tested IDS. We evaluate our approach on a denial-of-service attack detection scenario and a dataset recording the network traffic of a real-world system. Our experiments show that our search-based attack scheme generates successful attacks bypassing the current state-of-the-art defences. We also show that our approach is capable of generating attack patterns for all configuration states of the studied IDS and that it is capable of providing appropriate countermeasures. By co-evolving our attack and defence mechanisms we succeeded at improving the defence of the IDS under test by making it resilient to 49 out of 50 independently generated attacks.