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% of abnormal data, thereby enabling a proactive response, and can detect 99% of abnormal data at the final stage.

中文翻译：

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网络异常的分层检测：一种自我监督学习方法


随着互联网流量的不断增加，最近报道了大量的网络入侵事件。在这封信中，我们提出了一种网络入侵检测系统，可以实现基于自监督学习的分层检测。所提出的解决方案由多个检测阶段组成，包括早期检测极端异常值，这可能会对系统造成严重损坏。此外，它使用具有专门异常分数的隐藏空间进行彻底的重新检查，这导致了很高的检测精度。大量的仿真结果证实，所提出的解决方案可以先发制人地检测到20%的异常数据，从而实现主动响应，并且可以在最后阶段检测到99%的异常数据。