Abstract
Imbalance in data is quite obvious while studying intrusion detection system (IDS). Classification algorithms are used to identify the attacks in IDS, which has many parameters for its performance evaluation. Due to imbalance in data, the classification results need to be revisited given that IDS generally evaluates detection rate and false alarm rate which belongs to two different classes. This paper validates a new metric NPR used for ranking the classifiers for IDS. The metric is made functional on KDD data set and then the classifiers are ranked and compared with results on another data set.
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Aggarwal, P., Sharma, S.K. (2016). A Metric for Ranking the Classifiers for Evaluation of Intrusion Detection System. In: Satapathy, S., Raju, K., Mandal, J., Bhateja, V. (eds) Proceedings of the Second International Conference on Computer and Communication Technologies. Advances in Intelligent Systems and Computing, vol 380. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2523-2_44
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DOI: https://doi.org/10.1007/978-81-322-2523-2_44
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