Chaotic-based optimization, based feature selection with shallow neural network technique for effective identification of intrusion detection

Abstract

The work in this paper attempts to deal with intrusion detection using a chaotic-based optimization technique and feature selection + shallow neural networks. The idea of chaotic systems is used to get randomness in the feature selection process, which can enable a shallow neural network to perform better for intrusion detection. Experiments on benchmark datasets reveal the effectiveness of this proposed solution by significant improvements in detection accuracy, false positive reduction at run-time and computational efficiency as compared to conventional methods.