Comprehensive Analysis of Intrusion Detection Systems and their Associated Algorithms in Information Security

Abstract

The study primarily focuses on the analysis and comparison of intrusion detection techniques in network environments, with the objective of evaluating the impact of Intrusion Detection Systems (IDSs) on data protection. It also aims to understand how these techniques have adapted to emerging threats and evaluate their effectiveness in detecting malicious activities. To achieve this, a systematic review of documents stored in the IEEE Xplore, Redalyc, and ScienceDirect databases between 2019 and 2023 was conducted. The analysis reveals that intrusion detection techniques have evolved significantly to address constantly changing cyber threats. Specifically, machine learning-based techniques and behavior analysis have proven to be effective in reducing false positives. However, to keep up to date with the threats, constant management and updating of these techniques is required. Furthermore, it has been determined that intrusion detection is crucial for cybersecurity. This is because in a rapidly evolving cyber environment where threats swiftly advance, IDSs provide critical defense by offering continuous visibility and protection against potential intrusions or malicious activities in real-time.