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A Dynamic Subspace Anomaly Detection Method Using Generic Algorithm for Streaming Network Data

A Dynamic Subspace Anomaly Detection Method Using Generic Algorithm for Streaming Network Data

Ji Zhang
Copyright: © 2015 |Pages: 23
ISBN13: 9781466673816|ISBN10: 1466673818|EISBN13: 9781466673823
DOI: 10.4018/978-1-4666-7381-6.ch018
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MLA

Zhang, Ji. "A Dynamic Subspace Anomaly Detection Method Using Generic Algorithm for Streaming Network Data." Handbook of Research on Emerging Developments in Data Privacy, edited by Manish Gupta, IGI Global, 2015, pp. 403-425. https://doi.org/10.4018/978-1-4666-7381-6.ch018

APA

Zhang, J. (2015). A Dynamic Subspace Anomaly Detection Method Using Generic Algorithm for Streaming Network Data. In M. Gupta (Ed.), Handbook of Research on Emerging Developments in Data Privacy (pp. 403-425). IGI Global. https://doi.org/10.4018/978-1-4666-7381-6.ch018

Chicago

Zhang, Ji. "A Dynamic Subspace Anomaly Detection Method Using Generic Algorithm for Streaming Network Data." In Handbook of Research on Emerging Developments in Data Privacy, edited by Manish Gupta, 403-425. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-7381-6.ch018

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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 1999 KDD CUP anomaly detection application. Innovative approaches for training data generation, anomaly classification, false positive reduction, and adoptive detection subspace generation 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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