Application of Local Outlier Mining Based on Cluster Merging Algorithm in Intrusion Detection

doi:10.3969/j.issn.1006-2475.2015.08.013

Abstract

Abstract: According to the problem of high dimension of network security data, the traditional outliers based on cluster cannot effectively detect network intrusion behavior data in detail. This paper put forward an improved DBSCAN algorithm of outlier mining called LDBSCAN-CM. First, this paper introduced a concept of local outlier mining for traditional DBSCAN algorithm, calculated local outlier factors of candidate objects, and generated a number of clusters. Next, this paper merged clusters in order to improve the mining efficiency. Eventually, the KDD Cup99 dataset was applied to conduct simulation experiment on the application of the improved algorithm in intrusion detection. The results indicate that the improved algorithm LDBSCAN-CM can guarantee higher detection rate and lower false alarm rate.
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Key words: intrusion detection, data mining, LDBSCAN-CM, local outlier mining, clustering merger

CLC Number:

TP393

MEI Xiao-hui, LONG Yuan, ZHANG Jian-bo. Application of Local Outlier Mining Based on Cluster Merging Algorithm in Intrusion Detection[J]. Computer and Modernization, 2015, 0(8): 67-70.

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