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

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

Authors

Hatem Ahriz



Contributors

O. Makarevich
Editor

L. Babenko
Editor

M. Anikeev
Editor

A. Elci
Editor

H. Shahriar
Editor

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 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

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