PoS - Proceedings of Science
Volume 299 - The 7th International Conference on Computer Engineering and Networks (CENet2017) - Session V - Date Analysis
Detecting Anomalous User Behavior in Database
J. Chen*, J. Ai, L. Wei, J. Wang, H. He, C. Liang and L. Chen
Full text: pdf
Pre-published on: July 17, 2017
Published on: September 06, 2017
In order to protect vital data in today’s internet environment and
prevent misuse, especially insider abuse by valid users, we propose a novel two-step detecting approach to distinguish potential misuse behaviour (namely anomalous user behaviour) from normal behaviour. First, we capture the access patterns of users by using association rules. Then, based on the patterns and users’ sequential behaviour, we try to deter anomalous user behaviour by leveraging the logistic regression model. Experimental results on real dataset indicate that our method can get a better result and outperform two state-of-the-art method. The proposed two-step detecting approach can effectively detect anomalous user behaviour from the log data generated by
operation and maintenance staffs.
DOI: https://doi.org/10.22323/1.299.0096
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