Artificial Neural Network Approaches to Intrusion Detection: A Review

被引:0
|
作者
Ahmad, Iftikhar [1 ,2 ]
Abdullah, Azween B. [2 ]
Alghamdi, Abdullah S. [1 ]
机构
[1] King Saud Univ, Coll Comp & Informat Sci, Dept Software Engn, Riyadh, Saudi Arabia
[2] Univ Teknol PETRONAS, Dept Comp & Informat Sci, Perak, Malaysia
关键词
Artificial Neural Network; Intrusion Detection System; Anomaly Detection; False positive; Negative; ROC; Detection Rate; RMSE; IDA; MLP;
D O I
暂无
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
Intrusion detection systems arc the foremost tools for providing safety in computer and network-system. There are many limitations in traditional IDSs like time consuming statistical analysis, regular updating, non adaptive accuracy and flexibility. It is an Artificial Neural Network that supports an ideal specification of an Intrusive Detection System and is a solution to the problems of traditional IDSs. Therefore, An Artificial Neural Network inspired by nervous system has become an interesting tool in the applications of Intrusion Detection Systems due to promising features. Intrusion detection by Artificial Neural Networks is an ongoing area. In this paper, we provide an introduction and review of the Artificial Neural Network Approaches within Intrusion Detection, in addition to m; suggestions for future research. We also discuss on tools and datasets that are being used in Artificial Neural Network Intrusion Detection Systems. This review may help the researcher to develop new optimize approach in the field of Intrusion Detection.
引用
收藏
页码:200 / +
页数:2
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