A Comparative Study on Anomaly-Based Network Intrusion Detection System

We are living in an information era where it is not possible to complete a day without information systems. Heterogeneous systems and devices become parts of information processing, transformation, and transmission. Parallelly hackers acquire potential means to intrude into information systems. The war between makers and hackers for information is never-ending. Evolution of Intrusion Detection Systems (IDS) is gaining pace with the help of machine learning and deep learning techniques. In the recent past, numerous attempts have been made to diminish hacker attacks, and the results are published in the literature. In this paper, we tried to offer some knowledge to the research community in the form of a study on anomaly-based network intrusion detection systems using machine learning and related techniques. Previous papers are filtered for a common dataset named CSE-CIC-IDS2018. The dataset is a collection of logs at the University of New Brunswick’s servers, about various DoS attacks. All the selected papers are compared with respect to the performance of the applied algorithms, and the best algorithms for intrusion detection systems are derived. The study reveals factors that affect the performance of algorithms, where preprocessing is a dominant one along with class imbalance handling. This study certainly provides some knowledge on designing better IDS alternatives.