Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift

Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security consisting in the detection of hostile action based on the unusual nature of events observed on the Information System.In our previous work (presented at C\&ESAR 2018 and FIC 2019), we have associated deep neural networks auto-encoders for anomaly detection and graph-based events correlation to address major limitations in UEBA systems. This resulted in reduced false positive and false negative rates, improved alert explainability, while maintaining real-time performances and scalability. However, we did not address the natural evolution of behaviours through time, also known as concept drift. To maintain effective detection capabilities, an anomaly-based detection system must be continually trained, which opens a door to an adversary that can conduct the so-called "frog-boiling" attack by progressively distilling unnoticed attack traces inside the behavioural models until the complete attack is considered normal. In this paper, we present a solution to effectively mitigate this attack by improving the detection process and efficiently leveraging human expertise. We also present preliminary work on adversarial AI conducting deception attack, which, in term, will be used to help assess and improve the defense system. These defensive and offensive AI implement joint, continual and active learning, in a step that is necessary in assessing, validating and certifying AI-based defensive solutions.

Protection of an information system by artificial intelligence: a three-phase approach based on behaviour analysis to detect a hostile scenario

The analysis of the behaviour of individuals and entities (UEBA) is an area of artificial intelligence that detects hostile actions (e.g. attacks, fraud, influence, poisoning) due to the unusual nature of observed events, by affixing to a signature-based operation. A UEBA process usually involves two phases, learning and inference. Intrusion detection systems (IDS) available still suffer from bias, including over-simplification of problems, underexploitation of the AI potential, insufficient consideration of the temporality of events, and perfectible management of the memory cycle of behaviours. In addition, while an alert generated by a signature-based IDS can refer to the signature on which the detection is based, the IDS in the UEBA domain produce results, often associated with a score, whose explainable character is less obvious. Our unsupervised approach is to enrich this process by adding a third phase to correlate events (incongruities, weak signals) that are presumed to be linked together, with the benefit of a reduction of false positives and negatives. We also seek to avoid a so-called "boiled frog" bias inherent in continuous learning. Our first results are interesting and have an explainable character, both on synthetic and real data.