通过选择和转换特征来提高IDS的性能

I. Muttaqien, T. Ahmad
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引用次数: 15

摘要

处理信息时代的网络安全问题是绝对必要的,这样才能使网络中交换或存储的数据免受威胁。大量的研究表明,使用基于入侵检测系统(IDS)的机器学习可以用来克服准确性问题。降维方法优化检测过程也成为人们关注的焦点。但是,前人的研究结果还有待完善。在本文中,我们提出了一种通过限制簇的大小和使用子介质形成新特征来优化入侵检测过程的降维方法。研究结果表明,所提出的系统能够提供比现有系统更好的结果。这也可以通过灵敏度和特异性值的增加来描述。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Increasing performance of IDS by selecting and transforming features
Dealing with problems of network security in the information age is absolutely necessary so that data exchanged or stored in a network can be maintained from the threat. Numerous studies have shown that the use of Intrusion Detection System (IDS)-based machine learning can be used to overcome the accuracy problem. Dimensionality reduction approach to optimize the detection process has also become the focus. However, there is still a need to improve the results of previous research. In this paper, we propose a method to perform dimensionality reduction which can optimize the intrusion detection process by limiting the size of the clusters and the usage of sub-medoids to form new features. The results of this research show that the proposed system is able to provide better results than the existing. This can also be depicted by the increase of sensitivity and specificity values.
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