Feature Selection for Effective Anomaly-Based Intrusion Detection

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Executive Summary

Intrusion Detection system (IDs) has become the main research focus in the area of information security. Most of the existing IDs use all the features in the network packet to evaluate and look for known intrusive patterns. Some of these features are irrelevant and redundant. The drawback of this approach is a lengthy detection process and degrading performance of an ID system. This paper shows a new hybrid algorithm RSNNA (Rough Set Neural Network Algorithm) is used to significantly reduce a number of computer resources, both memory and CPU time, required to detect an attack. The algorithm uses Rough Set theory in order to select out feature reducts and a trained artificial neural network to identify any kind of new attaches.

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