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Recent advancements in Artificial Intelligence (AI) have brought newcapabilities to behavioural analysis (UEBA) for cyber-security consisting inthe detection of hostile action based on the unusual nature of events observedon the Information System.In our previous work (presented at C\&ESAR 2018 andFIC 2019), we have associated deep neural networks auto-encoders for anomalydetection and graph-based events correlation to address major limitations inUEBA systems. This resulted in reduced false positive and false negative rates,improved alert explainability, while maintaining real-time performances andscalability. However, we did not address the natural evolution of behavioursthrough time, also known as concept drift. To maintain effective detectioncapabilities, 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 tracesinside the behavioural models until the complete attack is considered normal.In this paper, we present a solution to effectively mitigate this attack byimproving the detection process and efficiently leveraging human expertise. Wealso 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 activelearning, in a step that is necessary in assessing, validating and certifyingAI-based defensive solutions.

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