Abstract
An Intrusion Detection System (IDS) is an important component of the defense-in-depth security mechanism in any computer network system. For assuring timely detection of intrusions from millions of connection records, it is important to reduce the number of connection features examined by the IDS, using feature selection or feature reduction techniques. In this scope, this paper presents the first application of a distinctive feature selection method based on neural networks to the problem of intrusion detection, in order to determine the most relevant network features, which is an important step towards constructing a lightweight anomaly-based intrusion detection system. The same procedure is used for feature selection and for attack detection, which gives more consistency to the method. We apply this method to a case study, on KDD dataset and show its advantages compared to some existing feature selection approaches. We then measure its dependence to the network architecture and the learning database.
Highlights
A network intrusion is any attempt or action aiming at compromising the confidentiality, integrity or availability of a computer or network
This article presents a feature selection method based on Neural Networks (NN), applied on the problem of classifying traffic features according to their relative contribution to attack detection
We introduce the features that need to be ranked as inputs of a feed-forward neural network used as a classifier that distinguishes attacks from normal traffic
Summary
A network intrusion is any attempt or action aiming at compromising the confidentiality, integrity or availability of a computer or network. Due to the rapidly increasing network traffic, it becomes of significant interest for an anomaly-based IDS to rank the importance of input features, since the elimination of irrelevant or useless inputs leads to a simplification of the problem and may allow faster and more accurate detection. For this aim, this article presents a feature selection method based on Neural Networks (NN), applied on the problem of classifying traffic features according to their relative contribution to attack detection.
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