Learning automata based SVM for intrusion detection

TL;DR

This paper introduces a novel LA-SVM method that uses learning automata to automatically eliminate redundant features, enhancing accuracy and efficiency in intrusion detection systems.

Contribution

It is the first application of learning automata for feature dimension reduction in intrusion detection, improving SVM performance.

Findings

LA-SVM achieves higher accuracy than traditional SVM.

LA-SVM is more efficient in prediction tasks.

Feature reduction simplifies training and speeds up detection.

Abstract

As an indispensable defensive measure of network security, the intrusion detection is a process of monitoring the events occurring in a computer system or network and analyzing them for signs of possible incidents. It is a classifier to judge the event is normal or malicious. The information used for intrusion detection contains some redundant features which would increase the difficulty of training the classifier for intrusion detection and increase the time of making predictions. To simplify the training process and improve the efficiency of the classifier, it is necessary to remove these dispensable features. in this paper, we propose a novel LA-SVM scheme to automatically remove redundant features focusing on intrusion detection. This is the first application of learning automata for solving dimension reduction problems. The simulation results indicate that the LA-SVM scheme achieves a higher accuracy and is more efficient in making predictions compared with traditional SVM.