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Applying Weighted PCA on Multiclass Classification for Intrusion Detection

Applying Weighted PCA on Multiclass Classification for Intrusion Detection

Mohsen Moshki, Mehran Garmehi, Peyman Kabiri
Copyright: © 2012 |Pages: 22
ISBN13: 9781609608361|ISBN10: 1609608364|EISBN13: 9781609608378
DOI: 10.4018/978-1-60960-836-1.ch009
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MLA

Moshki, Mohsen, et al. "Applying Weighted PCA on Multiclass Classification for Intrusion Detection." Privacy, Intrusion Detection and Response: Technologies for Protecting Networks, edited by Peyman Kabiri, IGI Global, 2012, pp. 220-241. https://doi.org/10.4018/978-1-60960-836-1.ch009

APA

Moshki, M., Garmehi, M., & Kabiri, P. (2012). Applying Weighted PCA on Multiclass Classification for Intrusion Detection. In P. Kabiri (Ed.), Privacy, Intrusion Detection and Response: Technologies for Protecting Networks (pp. 220-241). IGI Global. https://doi.org/10.4018/978-1-60960-836-1.ch009

Chicago

Moshki, Mohsen, Mehran Garmehi, and Peyman Kabiri. "Applying Weighted PCA on Multiclass Classification for Intrusion Detection." In Privacy, Intrusion Detection and Response: Technologies for Protecting Networks, edited by Peyman Kabiri, 220-241. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-60960-836-1.ch009

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Abstract

In this chapter, application of Principal Component Analysis (PCA) and one of its extensions on intrusion detection is investigated. This extended version of PCA is modified to cover an important shortcoming of traditional PCA. In order to evaluate these modifications, it is mathematically proved that these modifications are beneficial and later on a known dataset such as the DARPA99 dataset is used to verify results experimentally. To verify this approach, initially the traditional PCA is used to preprocess the dataset. Later on, using a simple classifier such as KNN, the effectiveness of the multiclass classification is studied. In the reported work, instead of traditional PCA, a revised version of PCA named Weighted PCA (WPCA) will be used for feature extraction. The results from applying the aforementioned method to the DARPA99 dataset show that this approach results in better accuracy than the traditional PCA when a number of features are limited, a number of classes are large, and a population of classes is unbalanced. In some situations WPCA outperforms traditional PCA by more than 1% in accuracy.

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