A Comprehensive Survey on Support Vector Machines for Intrusion Detection System

Abstract

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Machine learning is a widely interdisciplinary field centered on theories from cognitive science, computer science, statistics, optimization and many other theoretical and mathematical disciplines. Classification is a supervised learning technique used in machine learning to evaluate a given dataset and to create a model that divides data into a desired and distinct number of groups. The strength of SVMs lies in their use of nonlinear kernel features that map input into high-dimensional spaces of features implicitly. We’ll address the value of SVMs in this survey article. Discussing their SVM tuning parameters as well. The main purpose of this paper is to include detailed studies on SVM implementations by contrasting the current ML models with the SVM versions, also poses the problems of the intrusion detection method of the support vector machines, and also this paper provides researchers with a summary of the SVM that assists in their future analysis.

Keywords: Data Mining (DM), Intrusion Detection System (IDS), Machine Learning (ML), Optimization, Support Vector Machines (SVMs).

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