Title: Anomaly detection using fuzzy association rules

Authors: M. Dolores Ruiz; Maria J. Martin-Bautista; Daniel Sánchez; Maria-Amparo Vila; Miguel Delgado

Addresses: Department of Computer Science and A.I., CITIC-UGR, University of Granada, C/ Periodista Rafael Gómez, Granada, Spain ' Department of Computer Science and A.I., CITIC-UGR, University of Granada, C/ Periodista Rafael Gómez, Granada, Spain ' Department of Computer Science and A.I., CITIC-UGR, University of Granada, C/ Periodista Rafael Gómez, Granada, Spain; European Centre for Soft Computing, C/ Gonzalo Gutiérrez Quirós, Mieres, Spain ' Department of Computer Science and A.I., CITIC-UGR, University of Granada, C/ Periodista Rafael Gómez, Granada, Spain ' Department of Computer Science and A.I., CITIC-UGR, University of Granada, C/ Periodista Rafael Gómez, Granada, Spain

Abstract: Data mining techniques are a very important tool for extracting useful knowledge from databases. Recently, some approaches have been developed for mining novel kinds of useful information, such as anomalous rules. These kinds of rules are a good technique for the recognition of normal and anomalous behaviour, that can be of interest in several area domains such as security systems, financial data analysis, network traffic flow, etc. The aim of this paper is to propose an association rule mining process for extracting the common and anomalous patterns in data that is affected by some kind of imprecision or uncertainty, obtaining information that will be meaningful and interesting for the user. This is done by mining fuzzy anomalous rules. We present a new approach for mining such rules, and we apply it to the case of detecting normal and anomalous patterns on credit data.

Keywords: data mining; fuzzy association rules; anomalous rules; anomaly detection; credit data; imprecision; uncertainty.

DOI: 10.1504/IJESDF.2014.060171

International Journal of Electronic Security and Digital Forensics, 2014 Vol.6 No.1, pp.25 - 37

Received: 02 Nov 2013
Accepted: 08 Nov 2013

Published online: 27 Mar 2014 *

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