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 na´ve 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, Na´ve Bayes

  1. SANS Institute InfoSec Reading Room, “Understanding Intrusion Detection Systems”, Available: https://www.sans.org/reading- room/whitepapers/detection/understanding-intrusion-detection- systems-337, Accessed: November 2018].
  2. Sailesh Kumar, "Survey of Current Network Intrusion Detection Techniques", Available: http://www.cse.wustl.edu/~jain/cse571- 07/ftp/ids/, Accessed: November 2018].
  3. Jean Philippe Planquart, “Application of Neural Networks to Intrusion Detection”, SANS Institute InfoSec Reading Room.
  4. Ian H. Witten and Eibe Frank, “Data Mining: Practical Machine Learning Tools and Techniques”, Morgan Kaufmann Publishers, Publication Date: January 20, (2018) | ISBN-10: 0123748569 | ISBN- 13: 978-0123748560 | Edition: 3
  5. Binita Kumari andTripti Swarnkar, “Filter versus Wrapper Feature Subset Selection in Large Dimensionality Micro array: A Review”, International Journal of Computer Science and Information Technologies (IJCSIT), Vol. 2 (3) , 2019, pp.1048-1053
  6. Jasmina Novakovic?, Perica Strbac, Dusan Bulatovic?, “Toward optimal feature selection using Ranking methods and classification Algorithms”, Yugoslav Journal of Operations Research, Vol. 21, No. 1, pp.119-135, 2018.
  7. Deval Bhamare, Tara Salman, Mohammed Samaka, Aiman Erbad and Raj Jain, “Feasibility of Supervised Machine Learning for Cloud

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.
Journal URL : http://ijsrset.com/IJSRSET196372

Article Preview