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A Subspace-Based Analysis Method for Anomaly Detection in Large and High-Dimensional Network Connection Data Streams

A Subspace-Based Analysis Method for Anomaly Detection in Large and High-Dimensional Network Connection Data Streams

Ji Zhang
Copyright: © 2012 |Pages: 27
ISBN13: 9781609608361|ISBN10: 1609608364|EISBN13: 9781609608378
DOI: 10.4018/978-1-60960-836-1.ch008
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MLA

Zhang, Ji. "A Subspace-Based Analysis Method for Anomaly Detection in Large and High-Dimensional Network Connection Data Streams." Privacy, Intrusion Detection and Response: Technologies for Protecting Networks, edited by Peyman Kabiri, IGI Global, 2012, pp. 193-219. https://doi.org/10.4018/978-1-60960-836-1.ch008

APA

Zhang, J. (2012). A Subspace-Based Analysis Method for Anomaly Detection in Large and High-Dimensional Network Connection Data Streams. In P. Kabiri (Ed.), Privacy, Intrusion Detection and Response: Technologies for Protecting Networks (pp. 193-219). IGI Global. https://doi.org/10.4018/978-1-60960-836-1.ch008

Chicago

Zhang, Ji. "A Subspace-Based Analysis Method for Anomaly Detection in Large and High-Dimensional Network Connection Data Streams." In Privacy, Intrusion Detection and Response: Technologies for Protecting Networks, edited by Peyman Kabiri, 193-219. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-60960-836-1.ch008

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Abstract

A great deal of research attention has been paid to data mining on data streams in recent years. In this chapter, the authors carry out a case study of anomaly detection in large and high-dimensional network connection data streams using Stream Projected Outlier deTector (SPOT) that is proposed in (Zhang et al. 2009) to detect anomalies from data streams using subspace analysis. SPOT is deployed on the 1999 KDD CUP anomaly detection application. Innovative approaches for training data generation, anomaly classification, and false positive reduction are proposed in this chapter as well. Experimental results demonstrate that SPOT is effective and efficient in detecting anomalies from network data streams and outperforms existing anomaly detection methods.

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