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Feature combinations affect network intrusion detection/prevention systems based on machine learning methods such as multi-layer perceptron (MLP) in terms of accuracy and efficiency. However, selecting the optimal feature subset from the list of possible feature sets to detect network intrusions requires extensive computing resources. In this paper, we propose an optimal feature selection algorithm based on the simulated annealing algorithm to determine six denial of service attacks (neptune, teardrop, smurf, pod, back, land). In order to evaluate the performance of our proposed algorithm, three well-known machine learning methods (multi-layer perceptron., Bayes classifier, and support vector machine) are used against the NSL-KDD data set. Convergence Security, Vol. 1, 1–18. doi: 10.13052/jcs2445-9992.2016.001 c © 2016 River Publishers. All rights reserved. 2 I.-S. Jeong et al.

A Feature Selection Approach Based on Simulated Annealing for Detecting Various Denial of Service Attacks