Intrusion Detection System for Malicious Traffic Using Evolutionary Search Algorithm

Abstract

\n\n Intrusion detection systems play a key role in system security by identifying potential attacks and\ngiving appropriate responses. As new attacks are always emerging, intrusion detection systems must adapt to these\nattacks, and more work is continuously needed to develop and propose new methods and techniques that can improve\nefficient and effective adaptive intrusion systems. Feature selection is one of the challenging areas that need more work\nbecause of its importance and impact on the performance of intrusion detection systems. This paper applies evolutionary\nsearch algorithm in feature subset selection for intrusion detection systems.\n\n\n\n The evolutionary search algorithm for the feature subset selection is applied and two classifiers are used, Naïve\nBayes and decision tree J48, to evaluate system performance before and after features selection. NSL-KDD dataset and its\nsubsets are used in all evaluation experiments.\n\n\n\n The results show that feature selection using the evolutionary search algorithm enhances the intrusion detection\nsystem with respect to detection accuracy and detection of unknown attacks. Furthermore, time performance is achieved\nby reducing training time, which is reflected positively in overall system performance.\n\n\n\nThe evolutionary search applied to select IDS algorithm features can be developed by\nmodifying and enhancing mutation and crossover operators and applying new enhanced techniques\nin the selection process, which can give better results and enhance the performance of intrusion detection\nfor rare and complicated attacks.\n\n\n\nThe evolutionary search algorithm is applied to find the best subset of features for the\nintrusion detection system. In conclusion, it is a promising approach to be used as a feature selection\nmethod for intrusion detection. The results showed better performance for the intrusion detection\nsystem in terms of accuracy and detection rate.\n