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Intrusion detection with evolutionary learning classifier systems

Content Provider	Springer Nature Link
Author	Shafi, Kamran Kovacs, Tim Abbass, Hussein A. Zhu, Weiping
Copyright Year	2007
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.
Starting Page	3
Ending Page	27
Page Count	25
File Format	PDF
ISSN	15677818
Journal	Natural Computing
Volume Number	8
Issue Number	1
e-ISSN	15729796
Language	English
Publisher	Springer Netherlands
Publisher Date	2007-08-21
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Learning classifier systems LCS XCS UCS Intrusion Complexity Artificial Intelligence (incl. Robotics) Processor Architectures Evolutionary Biology Theory of Computation
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications

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