Abstract
As a platform for data storage and administration, database contains private and large information, which makes it a target of malicious personnel attacks. To prevent attacks from outsiders, database administrators can limit unauthorized user access through role-based access control system, while masquerade attacks from insiders are often less noticeable. Therefore, the research on database anomaly detection based on user behavior has important practical application value. In this paper, we proposed the anomaly detection system for securing database. We took advantage of a user profile construction method to describe database user query statements without user grouping. Then k-means and random tree were applied to the user profile. With the specified user profile constructed according to the characteristics of the query submitted by the user, the k-means is used to group the users. Then random tree algorithm is used to train anomaly detector. The experimental results show that this method proposed is fast and effective for detecting anomaly of database user behaviors.
This work is supported by the science and technology project of State Grid Corporation of China “Research on Key Technologies of dynamic identity security authentication and risk control in power business “ (Grand No. 5700-201972227A-0-0-00).
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Wang, X., Fang, Z., Wang, D., Feng, A., Wang, Q. (2020). Research on Database Anomaly Access Detection Based on User Profile Construction. In: Xu, G., Liang, K., Su, C. (eds) Frontiers in Cyber Security. FCS 2020. Communications in Computer and Information Science, vol 1286. Springer, Singapore. https://doi.org/10.1007/978-981-15-9739-8_30
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DOI: https://doi.org/10.1007/978-981-15-9739-8_30
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