Effective mining on large databases for intrusion detection

Citation

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).

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.

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Additional Metadata

Item Type:	Conference or Workshop Item (Paper)
Divisions:	Faculty of Computer Science and Information Technology
DOI Number:	https://doi.org/10.1109/ISBAST.2014.7013122
Publisher:	IEEE
Keywords:	Data mining; Apriori; Sequence mining; Database intrusion detection
Depositing User:	Nursyafinaz Mohd Noh
Date Deposited:	29 Jul 2015 08:27
Last Modified:	28 Jul 2016 08:55
Altmetrics:	http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1109/ISBAST.2014.7013122
URI:	http://psasir.upm.edu.my/id/eprint/39403
Statistic Details:	View Download Statistic

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