Learning automata based SVM for intrusion detection
This work addresses intrusion detection for network security, but it is incremental as it applies learning automata to a known dimension reduction problem.
The paper tackles the problem of redundant features in intrusion detection by proposing a novel LA-SVM scheme that automatically removes these features, resulting in higher accuracy and improved efficiency compared to traditional SVM.
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.