FG Simulation und Modellierung, Institute of Systems Engineering, G. W. Leibniz University of Hannover Welfengarten 1, 30167 Hannover, Germany

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Abstract—Adding security mechanisms to computer and communication systems without degrading their performance is a difficult task. This holds especially for wireless sensor networks, which due to their design are especially vulnerable to intrusion or attack. It is therefore important to find security mechanisms which deal with the limited resources of such systems in terms of energy consumption, computational capabilities and memory requirements.
In this document we discuss and evaluate several learning algorithms according to their suitability for intrusion and attack detection. Learning algorithms subject to evaluation include bio-inspired approaches such as Artificial Immune Systems or Neural Networks, and classical such as Decision Trees, Bayes classifier, Support Vector Machines, k-Nearest Neighbors and others. We conclude that, in our setup, the more simplistic approaches such as Decision Trees or Bayes classifier offer a reasonable performance. The performance was, however, found to be significantly dependent on the feature representation.

Index Terms—Attack, Intrusion and Anomaly Detection; Wireless Sensor Networks; Artificial Immune Systems; Machine Learning; Bio-Inspired Approach.

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Cite: On Classification Approaches for Misbehavior Detection in Wireless Sensor Networks, "Matthias Becker, Martin Drozda, Sven Schaust, Sebastian Bohlmann, Helena Szczerbicka," Journal of Computers vol. 4, no. 5, pp. 357-365, 2009.