Network Phenotyping for Network Traffic Classification and Anomaly Detection

Minhui Zou, Chengliang Wang, Fangyu Li, WenZhan Song

This paper proposes to develop a network phenotyping mechanism based on network resource usage analysis and identify abnormal network traffic. The network phenotyping may use different metrics in the cyber physical system (CPS), including resource and network usage monitoring, physical state estimation. The set of devices will collectively decide a holistic view of the entire system through advanced image processing and machine learning methods. In this paper, we choose the network traffic pattern as a study case to demonstrate the effectiveness of the proposed method, while the methodology may similarly apply to classification and anomaly detection based on other resource metrics. We apply image processing and machine learning on the network resource usage to extract and recognize communication patterns. The phenotype method is experimented on four real-world decentralized applications. With proper length of sampled continuous network resource usage, the overall recognition accuracy is about 99%. Additionally, the recognition error is used to detect the anomaly network traffic. We simulate the anomaly network resource usage that equals to 10%, 20% and 30% of the normal network resource usage. The experiment results show the proposed anomaly detection method is efficient in detecting each intensity of anomaly network resource usage.

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