期刊名称：International Journal of Advances in Soft Computing and Its Applications
印刷版ISSN：2074-8523
出版年度：2010
卷号：2
期号：3
出版社：International Center for Scientific Research and Studies
摘要：Intrusion Detection Systems (IDS) are developed to be the defense against security threats. Current signature based IDS like firewalls and anti viruses, which rely on labeled training data, generally cannot detect novel attacks. The purpose of this study is to enhance the detection rate by reducing the network traffic features and to investigate the feasibility of bio-inspired Immune Network approach for clustering different kinds of attacks and some novel attacks. Rough Set method was applied to reduce the dimension of features in DARPA KDD Cup 1999 intrusion detection dataset. Immune Network clustering was then applied using aiNet algorithm to cluster the data. Empirical study revealed that detection rate was enhanced when most significant features were used to represent input data. The finding also revealed that Immune Network clustering method is robust in detecting novel attacks in the absence of labels
