The increasing number of security attacks have inspired researchers to employ various classifiers, such as support vector machines (SVMs), to deal with them in Intrusion detection systems (IDSs). This paper presents a comprehensive study and investigation of the SVM-based intrusion detection and feature selection systems proposed in the literature. It first presents the essential concepts and background knowledge about security attacks, IDS, and SVM classifiers. It then provides a taxonomy of the SVM-based IDS schemes and describes how they have adapted numerous types of SVM classifiers in detecting various types of anomalies and intrusions. Moreover, it discusses the main contributions of the investigated schemes and highlights the algorithms and techniques combined with the SVM to enhance its detection rate and accuracy. Finally, different properties and limitations of the SVM-based IDS schemes are discussed.

越来越多的安全攻击促使研究人员采用各种分类器（例如支持向量机（SVM））在入侵检测系统（IDS）中进行处理。本文对文献中提出的基于SVM的入侵检测和特征选择系统进行了全面的研究和调查。它首先介绍有关安全攻击，IDS和SVM分类器的基本概念和背景知识。然后，它提供了基于SVM的IDS方案的分类法，并描述了它们如何适应多种类型的SVM分类器以检测各种类型的异常和入侵。此外，它讨论了所研究方案的主要贡献，并重点介绍了与SVM相结合的算法和技术，以提高其检测率和准确性。最后，