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Erschienen in:
2015 | OriginalPaper | Buchkapitel
TADOOP: Mining Network Traffic Anomalies with Hadoop
verfasst von : Geng Tian, Zhiliang Wang, Xia Yin, Zimu Li, Xingang Shi, Ziyi Lu, Chao Zhou, Yang Yu, Dan Wu
Erschienen in: Security and Privacy in Communication Networks
Verlag: Springer International Publishing
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Today, various anomalies and large number of flows in a network make traffic anomaly detection a big challenge. In this paper, we propose DTE-FP (Dual qTsallis Entropy for flow Feature with Properties), a more efficient method for traffic anomaly detection. To handle huge amount of traffic, based on Hadoop, we implement a network traffic anomaly detection system named TADOOP, which supports semi-automatic training and both offline and online traffic anomaly detection. TADOOP with a cluster of five servers has been deployed in Tsinghua University Campus Network. Furthermore, we compare DTE-FP with Tsallis entropy, and the experimental results show that DTE-FP has much better detection capability than Tsallis entropy.