Design network intrusion detection system using support vector machine,International Journal of Communication Systems

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Design network intrusion detection system using support vector machine
International Journal of Communication Systems ( IF 1.7 ) Pub Date : 2020-11-20 , DOI: 10.1002/dac.4689
Mahdi Ajdani ₁ , Hamidreza Ghaffary ₁

Affiliation

The growing use of the Internet and the existence of vulnerable points in networks have made the usage of intrusion detection systems as one of the most important security elements. This study aimed to present a method to design an analytical framework of detecting destructive data with respect to three factors including time, users' information, and scale. The design can be applied for big data. In the proposed method, to train data, the time has been divided into subperiods exploiting users' review information during each period of time, and the data have been trained. Also, storing methods have been applied for scalability to enhance the speed and reduce the volume of computations. The method used in this study is a combination of hardware‐software method to detect destructive data to cluster them (VIRUS TOTAL Dataset). Also, the proposed method applied a new algorithm of modified vector machine, and the efficiency of the algorithm has promoted support vector machine (SVM), designed to operate better than previous methods. The results showed that the proposed method is more acceptable than other previous methods. The results indicated that the method works with the accuracy of 0.97 which can be fairly accepted.

中文翻译：

支持向量机设计网络入侵检测系统

Internet的日益普及和网络中易受攻击点的存在已使入侵检测系统成为最重要的安全元素之一。这项研究旨在提出一种方法来设计一种分析框架，该框架可以根据时间，用户信息和规模三个因素来检测破坏性数据。该设计可以应用于大数据。在提出的方法中，为了训练数据，将时间划分为在每个时间段内利用用户的评论信息的子时段，并且对数据进行了训练。而且，已经将存储方法应用于可伸缩性以提高速度并减少计算量。本研究中使用的方法是硬件与软件方法的组合，用于检测破坏性数据以将其聚类（VIRUS TOTAL数据集）。也，该方法应用了一种新的改进矢量机算法，该算法的效率提高了支持矢量机（SVM），其设计性能优于以前的方法。结果表明，所提出的方法比其他先前方法更可接受。结果表明，该方法的准确度为0.97，可以接受。

更新日期：2021-01-04

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