The use of machine learning algorithms for detecting advanced persistent threats.

Eke, Hope Nkiruka; Petrovski, Andrei; Ahriz, Hatem

Abstract

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.

Citation

EKE, H.N., PETROVSKI, A. and AHRIZ, H. 2019. The use of machine learning algorithms for detecting advanced persistent threats. In Makarevich, O., Babenko, L., Anikeev, M., Elci, A. and Shahriar, H. (eds.). Proceedings of the 12th Security of information and networks international conference 2019 (SIN 2019), 12-15 September 2019, Sochi, Russia. New York: ACM [online], article No. 5. Available from: https://doi.org/10.1145/3357613.3357618

• Conference Name: 12th Security of information and networks international conference 2019 (SIN 2019)
• Conference Location: Sochi, Russia
• Start Date: Sep 12, 2019
• End Date: Sep 15, 2019
• Acceptance Date: Aug 9, 2019
• Online Publication Date: Sep 12, 2019
• Publication Date: Sep 30, 2019
• Deposit Date: Sep 17, 2019
• Publicly Available Date: Sep 17, 2019
• Publisher: ACM Association for Computing Machinery
• ISBN: 9781450372428
• DOI: https://doi.org/10.1145/3357613.3357618
• Keywords: Advanced persistent threats(APTs); Artificial immune system (AIS); Human immune system (HIS); Long short-term memory (LSTM); Recurrent neural network (RNN)
• Public URL: https://rgu-repository.worktribe.com/output/574490