Modern network intrusion detection systems rely on machine learning techniques to detect trac anomalies and thus intruders. However, the ability to learn the network behaviour in real-time comes at a cost: malicious software can interfere with the learning process, and teach the intrusion detection system to accept dangerous trac. This paper presents an intrusion detection system (IDS) that is able to detect common network attacks including but not limited to, denial-of-service, bot nets, intrusions, and network scans. With the help of the proposed example IDS, we show to what extent the training attack (and more sophisticated variants of it) has an impact on machine-learning based detection schemes, and how it can be detected.

No abstract

Security risk treatment often requires a complex cost-benefit analysis to be carried out in order to select countermeasures that optimally reduce risks while having minimal costs. According to ISO/IEC 27001, risk treatment relies on catalogues of countermeasures, and the analysts are expected to estimate the residual risks. At the same time, recent advancements in attack tree theory provide elegant solutions to this optimization problem. In this paper we propose to bridge the gap between these two worlds by introducing optimal countermeasure selection problem on attack-defense trees into the TRICK security risk assessment methodology.

No abstract

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. CCS CONCEPTS • Security and privacy → Intrusion detection systems; • Software and its engineering → Software testing and debugging.