Keyword Search:


Bookmark and Share

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

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

Full text not available from this repository.

Official URL: http://wseas.org/wseas/cms.action?id=10185

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.

Item Type:Article
Keyword:Database intrusion detection; Query profiling; Data mining; Apriori
Faculty or Institute:Faculty of Computer Science and Information Technology
Publisher:World Scientific and Engineering Academy and Society
ID Code:35941
Deposited By: Nurul Ainie Mokhtar
Deposited On:12 Feb 2016 10:30
Last Modified:12 Feb 2016 10:30

Repository Staff Only: Edit item detail

Document Download Statistics

This item has been downloaded for since 12 Feb 2016 10:30.

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