An Efficient ANN Deep Learning Model for Fraud Detection of Credit Card Users in Banking System Environment

Authors(2) :-Prof. Deepak Agrawal, Abhiruchi Dubey

A detection tool offers signs in competition to intrusion attacks wherein a conventional firewall fails. Tool learning algorithms purpose to find out anomalies the usage of supervised and unsupervised techniques. Competencies preference strategies identify important capabilities and cast off beside the point and redundant attributes to lessen the dimensionality of feature place. This work gives a capabilities desire framework for green community anomaly detection the usage of tremendous tool getting to know classifiers. The framework applies particular strategies with the useful beneficial useful resource of using filter and wrapper functions desire methodologies. The motive of this framework is to choose out the minimum form of functions that benefit the exceptional accuracy. Dataset is used in the experimental results to assess the proposed framework. The effects display that through manner of the usage of 18 functions from one of the clean out score techniques and making use of ANN and naïve bayes as a classifier, an accuracy of 86% is finished.

Authors and Affiliations

Prof. Deepak Agrawal
Takshshila Institute of Engineering and Technology, Jabalpur, Madhya Pradesh, India
Abhiruchi Dubey
Takshshila Institute of Engineering and Technology, Jabalpur, Madhya Pradesh, India

Intrusion detection system, Machine learning techniques, Features selection methods, ANN, Naïve Bayes

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Publication Details

Published in : Volume 6 | Issue 3 | May-June 2019
Date of Publication : 2019-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 358-363
Manuscript Number : IJSRSET196372
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Prof. Deepak Agrawal, Abhiruchi Dubey, " An Efficient ANN Deep Learning Model for Fraud Detection of Credit Card Users in Banking System Environment, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 6, Issue 3, pp.358-363, May-June-2019. Citation Detection and Elimination     |     
Journal URL : https://ijsrset.com/IJSRSET196372

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