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Effective mining on large databases for intrusion detection

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.

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Abstract

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.

Item Type:Conference or Workshop Item (Paper)
Keyword:Data mining; Apriori; Sequence mining; Database intrusion detection
Faculty or Institute:Faculty of Computer Science and Information Technology
Publisher:IEEE
DOI Number:10.1109/ISBAST.2014.7013122
Altmetrics:http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1109/ISBAST.2014.7013122
ID Code:39403
Deposited By: Nursyafinaz Mohd Noh
Deposited On:29 Jul 2015 16:27
Last Modified:28 Jul 2016 16:55

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