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User-independent and self-optimizing intrusion detection framework for large database systems


Citation

Adinehnia, Reza and Udzir, Nur Izura and Affendey, Lilly Suriani and Ishak, Iskandar and Mohd Hanapi, Zurina (2014) User-independent and self-optimizing intrusion detection framework for large database systems. WSEAS Transactions on Information Science and Applications, 12. art. no. 26. pp. 269-276. ISSN 1790-0832; ESSN: 2224-3402

Abstract / Synopsis

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.


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Additional Metadata

Item Type: Article
Divisions: Faculty of Computer Science and Information Technology
Publisher: World Scientific and Engineering Academy and Society
Keywords: Database intrusion detection; Query profiling; Data mining; Apriori
Depositing User: Nurul Ainie Mokhtar
Date Deposited: 12 Feb 2016 10:30
Last Modified: 12 Feb 2016 10:30
URI: http://psasir.upm.edu.my/id/eprint/35941
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