Machine Learning for Network Security: An Analysis of Intrusion Detection Systems

Abstract

Due to the increasing importance of data and communication over computer networks, securing the network has become a critical issue. One of the most common factors that attackers use to bypass security measures is the availability of Intrusion Detection System (IDS) techniques. This paper aims to introduce machine learning to help improve the capabilities of IDS. The paper explores the potential of machine learning to enhance the security of networks. It starts by introducing network security and IDS, and then it reviews the current limitations of IDS techniques. It then delves into the world of machine learning and its applications in IDS. The paper presents an overview of the various aspects of machine learning-based IDS, including the datasets used for analysis, the models used, and the evaluation metrics that were utilized to measure their effectiveness. It then compares the results with traditional IDS techniques, and it explores the potential of this technology for future research. According to the findings of the study, machine learning-based intrusion detection systems can provide an efficient and accurate method of detecting unauthorized access to a computer network. The paper concludes by discussing the technology's potential for securing networks.