Application of density-based outlier detection to database activity monitoring

Seung Kim, Nam Wook Cho (), Young Joo Lee, Suk-Ho Kang, Taewan Kim, Hyeseon Hwang and Dongseop Mun
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Seung Kim: Seoul National University
Nam Wook Cho: Seoul National University of Technology
Young Joo Lee: Seoul National University
Suk-Ho Kang: Seoul National University
Taewan Kim: Somansa Inc.
Hyeseon Hwang: Korea Atomic Energy Research Institute
Dongseop Mun: Korea Atomic Energy Research Institute

Information Systems Frontiers, 2013, vol. 15, issue 1, No 5, 55-65

Abstract: Abstract To prevent internal data leakage, database activity monitoring uses software agents to analyze protocol traffic over networks and to observe local database activities. However, the large size of data obtained from database activity monitoring has presented a significant barrier to effective monitoring and analysis of database activities. In this paper, we present database activity monitoring by means of a density-based outlier detection method and a commercial database activity monitoring solution. In order to provide efficient computing of outlier detection, we exploited a kd-tree index and an Approximated k-nearest neighbors (ANN) search method. By these means, the outlier computation time could be significantly reduced. The proposed methodology was successfully applied to a very large log dataset collected from the Korea Atomic Energy Research Institute (KAERI). The results showed that the proposed method can effectively detect outliers of database activities in a shorter computation time.

Keywords: Database monitoring; Density-based outlier detection; Intrusion detection; kd-tree; Approximated k-nearest neighbors (search for similar items in EconPapers)
Date: 2013
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10796-010-9266-9

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