Self-Supervised and Interpretable Anomaly Detection using Network Transformers

• URL: http://arxiv.org/abs/2202.12997v1
• Date: Fri, 25 Feb 2022 22:05:59 GMT
• Title: Self-Supervised and Interpretable Anomaly Detection using Network Transformers
• Authors: Daniel L. Marino, Chathurika S. Wickramasinghe, Craig Rieger, Milos Manic
• Abstract summary: This paper introduces the Network Transformer (NeT) model for anomaly detection. NeT incorporates the graph structure of the communication network in order to improve interpretability. The presented approach was tested by evaluating the successful detection of anomalies in an Industrial Control System.
• Score: 1.0705399532413615
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Monitoring traffic in computer networks is one of the core approaches for defending critical infrastructure against cyber attacks. Machine Learning (ML) and Deep Neural Networks (DNNs) have been proposed in the past as a tool to identify anomalies in computer networks. Although detecting these anomalies provides an indication of an attack, just detecting an anomaly is not enough information for a user to understand the anomaly. The black-box nature of off-the-shelf ML models prevents extracting important information that is fundamental to isolate the source of the fault/attack and take corrective measures. In this paper, we introduce the Network Transformer (NeT), a DNN model for anomaly detection that incorporates the graph structure of the communication network in order to improve interpretability. The presented approach has the following advantages: 1) enhanced interpretability by incorporating the graph structure of computer networks; 2) provides a hierarchical set of features that enables analysis at different levels of granularity; 3) self-supervised training that does not require labeled data. The presented approach was tested by evaluating the successful detection of anomalies in an Industrial Control System (ICS). The presented approach successfully identified anomalies, the devices affected, and the specific connections causing the anomalies, providing a data-driven hierarchical approach to analyze the behavior of a cyber network.

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