Application of Machine Learning Approaches in Intrusion Detection System - Abdulmunim Aziz, Zena; Mohsin Abdulazeez, Adnan (Journal of Soft Computing and Data Mining, 2021)
The rapid development of technology reveals several safety concerns for making life more straightforward. The advance of the Internet over the years has increased the number of attacks on the Internet. The IDS is one supporting layer for data protection. Intrusion Detection Systems (IDS) offers a healthy market climate and prevents misgivings in the network. Recently, IDS is used to recognize and distinguish safety risks using Machine Learning (ML). This paper proposed a comparative analysis of the different ML algorithms used in IDS and aims to identify intrusions with SVM, J48, and Naive Bayes. Intrusion is also classified. Work with the KDD-CUP data set, and their performance has checked with the Weak software. In comparison of techniques such as J48, SVM and Naïve Bayes showed that the accuracy of j48 is the higher one which was (99.96%).