A novel unsupervised anomaly detection based on robust principal component classifier

Wenbin Qiu; Yu Wu; Guoyin Wang; Simon X. Yang; Jie Bai; Jieying Li

doi:10.1117/12.664383

18 April 2006 A novel unsupervised anomaly detection based on robust principal component classifier

Wenbin Qiu, Yu Wu, Guoyin Wang, Simon X. Yang, Jie Bai, Jieying Li

Proceedings Volume 6241, Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2006; 62410T (2006) https://doi.org/10.1117/12.664383
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

Abstract

Intrusion Detection Systems (IDSs) need a mass of labeled data in the process of training, which hampers the application and popularity of traditional IDSs. Classical principal component analysis is highly sensitive to outliers in training data, and leads to poor classification accuracy. This paper proposes a novel scheme based on robust principal component classifier, which obtains principal components that are not influenced much by outliers. An anomaly detection model is constructed from the distances in the principal component space and the reconstruction error of training data. The experiments show that this proposed approach can detect unknown intrusions effectively, and has a good performance in detection rate and false positive rate especially.

Citation Download Citation

Wenbin Qiu, Yu Wu, Guoyin Wang, Simon X. Yang, Jie Bai, and Jieying Li "A novel unsupervised anomaly detection based on robust principal component classifier", Proc. SPIE 6241, Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2006, 62410T (18 April 2006); https://doi.org/10.1117/12.664383

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