A Systematic Review on Intrusion Detection System
Abstract
In the ever-evolving landscape of cyber threats, ensuring the security of internet-connected systems is of paramount importance. This study delves into the realm of cyber security, focusing on Intrusion Detection Systems (IDS) and their categorizations, mainly abuse (signature-based) and anomaly (behavior-based) detection. The article highlights the strengths and weaknesses of both methods and underscores the increasing need for machine learning strategies in cyber intrusion detection. Machine learning techniques offer promise in enhancing detection rates and minimizing false positives. Three main categories of anomaly-based IDS are examined: supervised, unsupervised, and semi-supervised. The study further explores and evaluates the performance of Support Vector Machine (SVM), Random Forest (RF), and Extreme Learning Machine (ELM) on the commonly used KDD dataset. A comprehensive review of recent contributions in the field is also presented, detailing the techniques used, datasets, accuracy rates, and associated limitations.
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Copyright (c) 2023 Vineeta Shrivastava
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