Enhancing Network Intrusion Detection Model Using Machine Learning Algorithms

After the digital revolution, large quantities of data have been generated with time through various networks. The networks have made the process of data analysis very difficult by detecting attacks using suitable techniques. While Intrusion Detection Systems (IDSs) secure resources against threats, they still face challenges in improving detection accuracy, reducing false alarm rates, and detecting the unknown ones. This paper presents a framework to integrate data mining classification algorithms and association rules to implement network intrusion detection. Several experiments have been performed and evaluated to assess various machine learning classifiers based on the KDD99 intrusion dataset. Our study focuses on several data mining algorithms such as; naïve Bayes, decision trees, support vector machines, decision tables, k-nearest neighbor algorithms, and artificial neural networks. Moreover, this paper is concerned with the association process in creating attack rules to identify those in the network audit data, by utilizing a KDD99 dataset anomaly detection. The focus is on false negative and false positive performance metrics to enhance the detection rate of the intrusion detection system. The implemented experiments compare the results of each algorithm and demonstrate that the decision tree is the most powerful algorithm as it has the highest accuracy (0.992) and the lowest false positive rate (0.009).