Hierarchical Detection of Network Anomalies : A Self-Supervised Learning Approach
Hyoseon Kye, Mi-Ru Kim, Minhae Kwon
Abstract
With the increasing amount of Internet traffic, a significant number of network intrusion events have recently been reported. In this letter, we propose a network intrusion detection system that enables hierarchical detection based on self-supervised learning. The proposed solution consists of multiple stages of detection, including the early detection of extreme outliers, which may cause severe damage to the system. Furthermore, it performs thorough reexaminations using the hidden spaces with specialized anomaly scores, which leads to high detection accuracy. Extensive simulation results confirm that the proposed solution can preemptively detect 20 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> of abnormal data, thereby enabling a proactive response, and can detect 99 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> of abnormal data at the final stage.