期刊名称：International Journal of Computer Science and Network Security
印刷版ISSN：1738-7906
出版年度：2017
卷号：17
期号：7
页码：315-319
出版社：International Journal of Computer Science and Network Security
摘要：Intrusion detection is a new network security mechanism for detecting, prevent-ting, and repelling unauthorized access to a communication or computer network. Intrusion detection systems (IDS) play a crucial role in maintaining a safe and secure network. One technical challenge in intrusion detection systems is the curse of high dimensionality. To overcome this problem, we propose a feature selection phase, which can be generally implemented in any intrusion detection system. In this work bat algorithm with k-means integrated to improving Intrusion Detection System accuracy. Experiments on KDD Cup 99 data set address that our proposed method results in detecting intrusions with higher accuracy, especially for remote to login (R2L) and user to remote (U2R) attacks.
关键词：component Intrusion detection; Bat Algorithm; clustering; k-measn
