Abstract
In intrusion detection, approaches incorporated with data mining become interesting nowadays, in particular the associative classification which is a hybrid technique which uses pruning measure. Although the full rules set is not intended for precise classification, the rules have been used effectively by classifiers that have been built in previous systems. Class detection by variance process uses the association rule mining concept for discovering the association among data variables, and the gained information about different patterns is used to classify the variables into different classes. Using the identified centroids, numerical data is discretized and fed to rule precision index (RPI) classifier for rule induction. Popular Data mining tools operate this technique in the name association based on classification (CBA) which uses confidence as an interest measure for rule pruning. In this work, we present a new interest measure named rule precision index (RPI) which helps us to prune association rules efficiently, and the impact is observed in the classification of attack and non-attack. The resultant associate method produces the best performance among association-based classifiers and is evaluated with conventional classifiers against three different intrusion detection datasets, namely NSL-KDD, CICIDS-2017 and KDD CUP99. The proposed RPI classifier, incorporated with novel interest measure, provides the best accuracy rate of 89.48 % on average than the available classifiers.
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Sivanantham, S., Mohanraj, V., Suresh, Y. et al. Rule precision index classifier: an associative classifier with a novel pruning measure for intrusion detection. Pers Ubiquit Comput 27, 1395–1403 (2023). https://doi.org/10.1007/s00779-021-01599-0
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DOI: https://doi.org/10.1007/s00779-021-01599-0