The Use of Machine Learning Algorithms for Detecting Advanced Persistent Threats

Advanced Persistent Threats (APTs) have been a major challenge in securing both Information Technology (IT) and Operational Technology (OT) systems. Due to their capability to navigates around defenses and to evade detection for a prolonged period of time, targeted APT attacks present an increasing concern for both cyber security and business continuity personnel. This paper explores the application of Artificial Immune System (AIS) and Recurrent Neural Networks (RNNs) variants for APT detection. It has been shown that the variants of the suggested algorithms provide not only detection capability, but can also classify malicious data traffic with respect to the type of APT attacks.