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

View Sample PDF

Author(s): Ji Zhang (University of Southern Queensland, Australia)
Copyright: 2015
Pages: 23
Source title: Handbook of Research on Emerging Developments in Data Privacy
Source Author(s)/Editor(s): Manish Gupta (State University of New York at Buffalo, USA)
DOI: 10.4018/978-1-4666-7381-6.ch018

Keywords: Information Science Reference / Information Security & Privacy / IT Security and Ethics / Security & Forensics

Purchase

View A Dynamic Subspace Anomaly Detection Method Using Generic Algorithm for Streaming Network Data on the publisher's website for pricing and purchasing information.

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.

The IRMA Community

Research IRM

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

Purchase

Abstract

Related Content

IRMA Sponsors