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2008
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Anomaly detection in high-dimensional network data streams: A case study

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Anomaly detection in high-dimensional network data streams: A case study
In this paper, we study the problem of anomaly detection in high-dimensional network streams. We have developed a new technique, called Stream Projected Ouliter deTector (SPOT), to deal with the problem of anomaly detection from high-dimensional data streams. We conduct a case study of SPOT in this paper by deploying it on 1999 KDD Intrusion Detection application. Innovative approaches for training data generation, anomaly classification and false positive reduction are proposed in this paper as well. Experimental results demonstrate that SPOT is effective in detecting anomalies from network data streams and outperforms existing anomaly detection methods.
Ji Zhang, Qigang Gao, Hai H. Wang
Added 26 Jan 2011
Updated 26 Jan 2011
Type Journal
Year 2008
Where ISI
Authors Ji Zhang, Qigang Gao, Hai H. Wang
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