User-Independent and Self-Optimizing Intrusion Detection Framework for Large Database Systems

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Abstract: Despite various access control approaches, databases are still vulnerable to intruders who are able to bypass these protective methods and access data, or prevent insiders like authorized users who misuse their privilege. To prevent all such intrusions, this study proposes a multilayer profiling method to provide suitable and reliable valid patterns to be used in the proposed database intrusion detection framework. With the help of association rule learning and Naive Bayes classifier this framework can provide a considerable rate of intrusion detection. The main contributions of this paper are summarized in a granular profiling structure and a detection framework that helps to detect database intrusions even if they are initiated by insiders.

WSEAS Transactions on Information Science and Applications, ISSN / E-ISSN: 1790-0832 / 2224-3402, Volume 12, 2015, Art. #26

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Reza Adinehnia, Nur Izura Udzir, Lilly Suriani Affendey, Iskandar Ishak, Zurina Mohd Hanapi, "User-Independent and Self-Optimizing Intrusion Detection Framework for Large Database Systems," WSEAS Transactions on Information Science and Applications, vol. 12, pp. 269-276, 2015, DOI:
Reza Adinehnia, Nur Izura Udzir, Lilly Suriani Affendey, Iskandar Ishak, Zurina Mohd Hanapi. User-Independent and Self-Optimizing Intrusion Detection Framework for Large Database Systems. WSEAS Transactions on Information Science and Applications. 2015;12:269-276.
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