文章基本信息

标题：Computational neural network regression model for Host based Intrusion Detection System
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作者：Sunil Kumar Gautam ; Sunil Kumar Gautam ; Hari Om 等
期刊名称：Perspectives in Science
印刷版ISSN：2213-0209
电子版ISSN：2213-0209
出版年度：2016
卷号：8
页码：93-95
DOI：10.1016/j.pisc.2016.04.005
语种：English
出版社：Elsevier
摘要：Summary The current scenario of information gathering and storing in secure system is a challenging task due to increasing cyber-attacks. There exists computational neural network techniques designed for intrusion detection system, which provide security to single machine and entire network's machine. In this paper, we have used two types of computational neural network models, namely, Generalized Regression Neural Network (GRNN) model and Multilayer Perceptron Neural Network (MPNN) model for Host based Intrusion Detection System using log files that are generated by a single personal computer. The simulation results show correctly classified percentage of normal and abnormal (intrusion) class using confusion matrix. On the basis of results and discussion, we found that the Host based Intrusion Systems Model (HISM) significantly improved the detection accuracy while retaining minimum false alarm rate.
关键词：Intrusion detection; Generalized Regression Neural Network; Multilayer Perceptron Neural Network; Confusion matrix;