The dual negative selection algorithm and its application for network anomaly detection Online publication date: Fri, 28-Jul-2017
by Xufei Zheng; Yanhui Zhou; Yonghui Fang
International Journal of Information and Communication Technology (IJICT), Vol. 11, No. 1, 2017
Abstract: Negative selection algorithm (NSA) is an important artificial immune detectors generation method for network anomaly detection. In this paper, we put forward the dual negative selection algorithm (DNSA) which includes two negative selection processes. In the first negative selection process, every randomly generated candidate detector tolerates with mature detector set and becomes semi-mature detector when not matches with any existing mature detectors. In the second negative selection process, the semi-mature detector tolerates with self set and becomes mature detector when not matches with any self element. The DNSA avoids the unnecessary and time-consuming self-tolerance process of candidate detector within the coverage of existing mature detectors, thus greatly reduces detector set size, and significantly improves detector generation efficiency. Theoretical analysis and simulations show that the DNSA effectively improves detector generation efficiency, and more suitable for network anomaly detection than traditional NSAs.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Information and Communication Technology (IJICT):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com