Hope Nkiruka Eke
The use of machine learning algorithms for detecting advanced persistent threats.
Eke, Hope Nkiruka; Petrovski, Andrei; Ahriz, Hatem
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.
|Start Date||Sep 12, 2019|
|Publication Date||Sep 30, 2019|
|Publisher||Association for Computing Machinery|
|Institution Citation||EKE, H.N., PETROVSKI, A. and AHRIZ, H. 2019. The use of machine learning algorithms for detecting advanced persistent threats. In Proceedings of the 12th International conference on security of information and networks (SIN 2019), 12-15 September 2019, Sochi, Russia. New York: ACM [online], article No. 5. Available from: 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)|
EKE 2019 The use of machine
© ACM 2019.
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