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Journal Articles Pattern Recognition Year : 2021

A simple graph embedding for anomaly detection in a stream of heterogeneous labeled graphs

Abstract

In this work, we propose a new approach to detect anomalous graphs in a stream of directed and labeled heterogeneous edges. The stream consists of a sequence of edges derived from different graphs. Each of these dynamic graphs represents the evolution of a specific activity in a monitored system whose events are acquired in real-time. Our approach is based on graph clustering and uses a simple graph embedding based on substructures and graph edit distance. Our graph representation is flexible and updates incrementally the graph vectors as soon as a new edge arrives. This allows the detection of anomalies in real-time which is an important requirement for sensitive applications such as cyber-security. Our implementation results prove the effectiveness of our approach in terms of accuracy of detection and time processing.
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Dates and versions

hal-02993787 , version 1 (02-11-2021)

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Abd Errahmane Kiouche, Sofiane Lagraa, Karima Amrouche, Hamida Seba. A simple graph embedding for anomaly detection in a stream of heterogeneous labeled graphs. Pattern Recognition, 2021, pp.107746. ⟨10.1016/j.patcog.2020.107746⟩. ⟨hal-02993787⟩
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