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Impact of PDS Based kNN Classifiers on Kyoto Dataset

Impact of PDS Based kNN Classifiers on Kyoto Dataset

Kailasam Swathi, Bobba Basaveswara Rao
Copyright: © 2019 |Volume: 6 |Issue: 2 |Pages: 12
ISSN: 2334-4598|EISSN: 2334-4601|EISBN13: 9781522568452|DOI: 10.4018/IJRSDA.2019040105
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MLA

Swathi, Kailasam, and Bobba Basaveswara Rao. "Impact of PDS Based kNN Classifiers on Kyoto Dataset." IJRSDA vol.6, no.2 2019: pp.61-72. http://doi.org/10.4018/IJRSDA.2019040105

APA

Swathi, K. & Rao, B. B. (2019). Impact of PDS Based kNN Classifiers on Kyoto Dataset. International Journal of Rough Sets and Data Analysis (IJRSDA), 6(2), 61-72. http://doi.org/10.4018/IJRSDA.2019040105

Chicago

Swathi, Kailasam, and Bobba Basaveswara Rao. "Impact of PDS Based kNN Classifiers on Kyoto Dataset," International Journal of Rough Sets and Data Analysis (IJRSDA) 6, no.2: 61-72. http://doi.org/10.4018/IJRSDA.2019040105

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Abstract

This article compares the performance of different Partial Distance Search-based (PDS) kNN classifiers on a benchmark Kyoto 2006+ dataset for Network Intrusion Detection Systems (NIDS). These PDS classifiers are named based on features indexing. They are: i) Simple PDS kNN, the features are not indexed (SPDS), ii) Variance indexing based kNN (VIPDS), the features are indexed by the variance of the features, and iii) Correlation coefficient indexing-based kNN (CIPDS), the features are indexed by the correlation coefficient of the features with a class label. For comparative study between these classifiers, the computational time and accuracy are considered performance measures. After the experimental study, it is observed that the CIPDS gives better performance in terms of computational time whereas VIPDS shows better accuracy, but not much significant difference when compared with CIPDS. The study suggests to adopt CIPDS when class labels were available without any ambiguity, otherwise it suggested the adoption of VIPDS.