Unsupervised anomaly intrusion detection using ant colony clustering model

被引：0

作者：

Tsang, W ^{[1
]}

Kwong, S ^{[1
]}

机构：

[1] City Univ Hong Kong, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China

来源：

Soft Computing as Transdisciplinary Science and Technology | 2005年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we present an efficient and biologically inspired clustering model for anomaly intrusion detection. The proposed model called Ant Colony Clustering Model (ACCM) that improves existing ant-based clustering model in searching for optimal clustering heuristically. Experimental results on KDD-Cup99 benchmark data show that ACCM is effective to detect known and unseen attacks with high detection rate and low false positive rate.

引用

页码：223 / 232

页数：10

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