Performance Analysis of Machine Learning Algorithms in Intrusion Detection System: A Review
T. Saranya, S. Sridevi, C. Deisy, Tran Duc Chung, M.K.A. Ahamed Khan
Abstract
The rapid growth of technologies not only formulates life easier but also exposes a lot of security issues. With the advancement of the Internet over years, the number of attacks over the Internet has been increased. Intrusion Detection System (IDS) is one of the supportive layers applicable to information security. IDS provide a salubrious environment for business and keeps away from suspicious network activities. Recently, Machine Learning (ML) algorithms are applied in IDS in order to identify and classify the security threats. This paper explores the comparative study of various ML algorithms used in IDS for several applications such as fog computing, Internet of Things (IoT), big data, smart city, and 5G network. In addition, this work also aims for classifying the intrusions using ML algorithms like Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART) and Random Forest. The work was tested with the KDD-CUP dataset and their efficiency was measured and also compared along with the latest researches.