User-independent and self-optimizing intrusion detection framework for large database systems
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 met...
| Main Authors: | , , , , |
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| Format: | Article |
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World Scientific and Engineering Academy and Society
2014
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| Online Access: | http://psasir.upm.edu.my/id/eprint/35941/ |
| _version_ | 1848848195147268096 |
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| author | Adinehnia, Reza Udzir, Nur Izura Affendey, Lilly Suriani Ishak, Iskandar Mohd Hanapi, Zurina |
| author_facet | Adinehnia, Reza Udzir, Nur Izura Affendey, Lilly Suriani Ishak, Iskandar Mohd Hanapi, Zurina |
| author_sort | Adinehnia, Reza |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T09:30:38Z |
| format | Article |
| id | upm-35941 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T09:30:38Z |
| publishDate | 2014 |
| publisher | World Scientific and Engineering Academy and Society |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-359412016-02-12T02:30:25Z http://psasir.upm.edu.my/id/eprint/35941/ User-independent and self-optimizing intrusion detection framework for large database systems Adinehnia, Reza Udzir, Nur Izura Affendey, Lilly Suriani Ishak, Iskandar Mohd Hanapi, Zurina 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. World Scientific and Engineering Academy and Society 2014 Article NonPeerReviewed 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 http://wseas.org/wseas/cms.action?id=10185 |
| spellingShingle | Adinehnia, Reza Udzir, Nur Izura Affendey, Lilly Suriani Ishak, Iskandar Mohd Hanapi, Zurina User-independent and self-optimizing intrusion detection framework for large database systems |
| title | User-independent and self-optimizing intrusion detection framework for large database systems |
| title_full | User-independent and self-optimizing intrusion detection framework for large database systems |
| title_fullStr | User-independent and self-optimizing intrusion detection framework for large database systems |
| title_full_unstemmed | User-independent and self-optimizing intrusion detection framework for large database systems |
| title_short | User-independent and self-optimizing intrusion detection framework for large database systems |
| title_sort | user-independent and self-optimizing intrusion detection framework for large database systems |
| url | http://psasir.upm.edu.my/id/eprint/35941/ http://psasir.upm.edu.my/id/eprint/35941/ |