NDLI: Using Density-Based Incremental Clustering for Anomaly Detection

Content Provider	IEEE Xplore Digital Library
Author	Fei Ren Liang Hu Hao Liang Xiaobo Liu Weiwu Ren
Copyright Year	2008
Description	Author affiliation: Coll. of Comput. Sci. & Technol., Jilin Univ., Changchun (Fei Ren; Liang Hu; Hao Liang; Xiaobo Liu; Weiwu Ren)
Abstract	This paper proposed a new anomaly detection algorithm that can update normal profile of system usage pattern dynamically. The feature used to model systempsilas usage pattern was program behavior. When system usage pattern changed, new program behaviors will be inserted into old profiles by density-based incremental clustering. Compared to traditional re-clustering updating, it is much more efficiently. Experiments with 1998 DARPA BSM audit data, shows that normal profiles generated by our algorithm is less sensitive to noise data objects than profile generated by analogous incremental algorithm ADWICE. So our algorithm shows an incremental detection quality and a much lower false alarm rate.
Starting Page	986
Ending Page	989
File Size	197812
Page Count	4
File Format	PDF
ISBN	9780769533360
DOI	10.1109/CSSE.2008.811
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Shape program behavior Educational institutions Spatial databases anomaly detection Noise generators normal profile Computer science Information science incremental clustering Clustering algorithms Intrusion detection Detection algorithms Software engineering
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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