Effective mining on large databases for intrusion detection
Data mining is a common automated way of generating normal patterns for intrusion detection systems. In this work a large dataset is customized to be suitable for both sequence mining and association rule learning. These two different mining methods are then tested and compared to find out which one...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2014
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| Online Access: | http://psasir.upm.edu.my/id/eprint/39403/1/Effective%20mining%20on%20large%20databases%20for%20intrusion%20detection.pdf |
| _version_ | 1825949403573125120 |
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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 |
| collection | UPM |
| description | Data mining is a common automated way of generating normal patterns for intrusion detection systems. In this work a large dataset is customized to be suitable for both sequence mining and association rule learning. These two different mining methods are then tested and compared to find out which one produces more accurate valid patterns for the intrusion detection system. Results show that higher detection rate is achieved when using apriori algorithm on the proposed dataset. The main contribution of this work is the evaluation of the association rule learning that can be used for further studies in the field of database intrusion detection systems. |
| first_indexed | 2024-03-06T08:44:02Z |
| format | Conference or Workshop Item |
| id | upm.eprints-39403 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T08:44:02Z |
| publishDate | 2014 |
| publisher | IEEE |
| record_format | dspace |
| spelling | upm.eprints-394032016-07-28T08:55:50Z http://psasir.upm.edu.my/id/eprint/39403/ Effective mining on large databases for intrusion detection Adinehnia, Reza Udzir, Nur Izura Affendey, Lilly Suriani Ishak, Iskandar Mohd Hanapi, Zurina Data mining is a common automated way of generating normal patterns for intrusion detection systems. In this work a large dataset is customized to be suitable for both sequence mining and association rule learning. These two different mining methods are then tested and compared to find out which one produces more accurate valid patterns for the intrusion detection system. Results show that higher detection rate is achieved when using apriori algorithm on the proposed dataset. The main contribution of this work is the evaluation of the association rule learning that can be used for further studies in the field of database intrusion detection systems. IEEE 2014 Conference or Workshop Item PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/39403/1/Effective%20mining%20on%20large%20databases%20for%20intrusion%20detection.pdf Adinehnia, Reza and Udzir, Nur Izura and Affendey, Lilly Suriani and Ishak, Iskandar and Mohd Hanapi, Zurina (2014) Effective mining on large databases for intrusion detection. In: International Symposium on Biometrics and Security Technologies (ISBAST 2014), 26-27 Aug. 2014, Kuala Lumpur, Malaysia. (pp. 204-207). 10.1109/ISBAST.2014.7013122 |
| spellingShingle | Adinehnia, Reza Udzir, Nur Izura Affendey, Lilly Suriani Ishak, Iskandar Mohd Hanapi, Zurina Effective mining on large databases for intrusion detection |
| title | Effective mining on large databases for intrusion detection |
| title_full | Effective mining on large databases for intrusion detection |
| title_fullStr | Effective mining on large databases for intrusion detection |
| title_full_unstemmed | Effective mining on large databases for intrusion detection |
| title_short | Effective mining on large databases for intrusion detection |
| title_sort | effective mining on large databases for intrusion detection |
| url | http://psasir.upm.edu.my/id/eprint/39403/1/Effective%20mining%20on%20large%20databases%20for%20intrusion%20detection.pdf |
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