TY  - JOUR
T1  - Optimization of Fuzzy Rules for Online Fraud Detection with the
Use of Developed Genetic Algorithm and Fuzzy Operators
AU - Parsaei, Mohammad Reza AU - Javidan, Reza AU - Sobouti, Mohammad Javad 
JO  - Asian Journal of Information Technology
VL  - 15
IS  - 11
SP  - 1856
EP  - 1864
PY  - 2016
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2016.1856.1864
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2016.1856.1864
KW  - Online auction
KW  -fraud detection
KW  -fuzzy rule
KW  -social network analysis
KW  -genetic algorithm
AB  - The huge numbers of users participating in online auctions and the low cost of creating accounts
for such activities have increased the danger of fraud and other criminal endeavors in these environments and
have pushed the monetary and financial institutions to seek efficient and quick solutions to detect such
offenses. This issue has necessitated the use of fraud detection techniques to prevent fraudulent endeavors
in banking and especially electronic banking systems. The objective of the present study was to propose a
hybrid approach to detect the fraudulent accounts. This objective was pursued by analyzing the social
networks to produce behavioral characteristics and then turning these characteristics into fuzzy rules. The fuzzy
rules proposed by the genetic algorithm were then optimized for auction fraud detection model. The
introduction of fuzzy crossover and mutation operators specifically modified for this objective was the other
contribution of this research to the literature. The results obtained by the implementation of the proposed
system showed that using these fuzzy crossover and mutation operators improved the algorithm performance
and the speed by which algorithm obtained the optimal solution.
ER  -