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