Evaluation of Machine Learning Algorithms for Intrusion Detection System
This work addresses the challenge of improving detection rates in intrusion detection systems, but it is incremental as it applies existing methods to a standard dataset.
The paper evaluated multiple machine learning classifiers on the KDD dataset for intrusion detection, finding that the decision table classifier minimized false negatives and random forest achieved the highest average accuracy.
Intrusion detection system (IDS) is one of the implemented solutions against harmful attacks. Furthermore, attackers always keep changing their tools and techniques. However, implementing an accepted IDS system is also a challenging task. In this paper, several experiments have been performed and evaluated to assess various machine learning classifiers based on KDD intrusion dataset. It succeeded to compute several performance metrics in order to evaluate the selected classifiers. The focus was on false negative and false positive performance metrics in order to enhance the detection rate of the intrusion detection system. The implemented experiments demonstrated that the decision table classifier achieved the lowest value of false negative while the random forest classifier has achieved the highest average accuracy rate.