Abstract
Data mining is the non-trivial process of identifying novel, potentially useful and understandable patterns in data. With most of the organizations starting on-line operations, the threat of security breaches is increasing. Since a database stores a lot of valuable information, its security has become paramount. One mechanism to safeguard the information in these databases is to use an intrusion detection system(IDS). In every database, there are a few attributes or columns that are more important to be tracked or sensed for malicious modifications as compared to the other attributes. In this paper, we propose an intrusion detection algorithm named weighted data dependency rule miner (WDDRM) for finding dependencies among the data items. The transactions that do not follow the extracted data dependency rules are marked as malicious. We show that WDDRM handles the modification of sensitive attributes quite accurately.
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Srivastava, A., Sural, S., Majumdar, A.K. (2006). Weighted Intra-transactional Rule Mining for Database Intrusion Detection. In: Ng, WK., Kitsuregawa, M., Li, J., Chang, K. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2006. Lecture Notes in Computer Science(), vol 3918. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731139_71
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DOI: https://doi.org/10.1007/11731139_71
Publisher Name: Springer, Berlin, Heidelberg
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