The Use of Machine Learning Algorithms for Detecting Advanced Persistent Threats

被引:7
|
作者
Eke, Hope Nkiruka [1 ]
Petrovski, Andrei [1 ]
Ahriz, Hatem [1 ]
机构
[1] Robert Gordon Univ, Sch Comp Sci & Digital Media, Aberdeen, Scotland
关键词
Advanced Persistent Threats(APTs); Artificial Immune System (AIS); Human Immune System (HIS); Long Short-Term Memory (LSTM); Recurrent Neural Network (RNN); ANOMALY DETECTION; NETWORK;
D O I
10.1145/3357613.3357618
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页数:8
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