Development of Rough Set - Hypergraph Technique for Key Feature Identification in Intrusion Detection Systems

•This paper presents a novel feature selection technique based on Rough Set Theory and Hypergraph (RSHGT) to identify the optimal feature subset for IDS.•RSHGT uses hypergraph technique to bring out the higher order relations between the features during the initial stage of data representation.•The performance validation of RSHGT was carried out with respect to the reduct size, time complexity, and classification accuracy.•The exploitation of vertex linearity and minimal transversal property of hypergraph resulted in a notable difference with respect to the time complexity and classification accuracy.

'Curse of dimensionality' - an unresolved challenge in the design of an intelligent system makes dimensionality reduction a significant topic of research for the identification of informative features from high-dimensional data sets. This paper presents a novel feature selection technique based on Rough Sets (RS) and few interesting properties of Hypergraph (RSHGT), such as minimal transversal and vertex linearity for the identification of the optimal feature subset. Experiments were carried out using KDD cup 1999 intrusion dataset obtained from the UCI repository. Validation using Weka tool shows the dominance of RSHGT over the existing feature selection techniques with respect to the reduct size, classifier accuracy and time complexity. To summarize, RSHGT was found to be flexible, accommodative and computationally attractive for high dimensional data sets.

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