Intrusion detection with evolutionary learning classifier systems |
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Authors: | Kamran Shafi Tim Kovacs Hussein A Abbass Weiping Zhu |
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Affiliation: | (1) School of Information Technology and Electrical Engineering, UNSW@ADFA, Canberra, Australia;(2) Department of Computer Science, University of Bristol, Bristol, BS8 1UB, UK |
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Abstract: | Evolutionary Learning Classifier Systems (LCSs) combine reinforcement learning or supervised learning with effective genetics-based
search techniques. Together these two mechanisms enable LCSs to evolve solutions to decision problems in the form of easy
to interpret rules called classifiers. Although LCSs have shown excellent performance on some data mining tasks, many enhancements are still needed to tackle features
like high dimensionality, huge data sizes, non-uniform distribution of classes, etc. Intrusion detection is a real world problem
where such challenges exist and to which LCSs have not previously been applied. An intrusion detection problem is characterised
by huge network traffic volumes, difficult to realize decision boundaries between attacks and normal activities and highly
imbalanced attack class distribution. Moreover, it demands high accuracy, fast processing times and adaptability to a changing
environment. We present the results and analysis of two classifier systems (XCS and UCS) on a subset of a publicly available
benchmark intrusion detection dataset which features serious class imbalances and two very rare classes. We introduce a better
approach for handling the situation when no rules match an input on the test set and recommend this be adopted as a standard
part of XCS and UCS. We detect little sign of overfitting in XCS but somewhat more in UCS. However, both systems tend to reach
near-best performance in very few passes over the training data. We improve the accuracy of these systems with several modifications
and point out aspects that can further enhance their performance. We also compare their performance with other machine learning
algorithms and conclude that LCSs are a competitive approach to intrusion detection.
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Keywords: | Learning classifier systems LCS XCS UCS Intrusion |
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