A Hybrid Simulated Annealing and Back-propagation Algorithm for Feed-forward Neural Network to Detect Credit Card Fraud
DOI:
https://doi.org/10.21928/uhdjst.v1n2y2017.pp31-36Keywords:
Artificial Neural Network, Back-propagation, Back-propagation Feed-forward Neural Network, Feed- forward Neural Network, Simulated Annealing, Simulated Annealing-back-propagation Feed-forward NeuralAbstract
Due to the ascent and fast development of E-commerce, utilization of credit cards for online buys has significantly expanded, and it brought about a blast in the credit card fraud. As credit card turns into the most prevalent method of installment for both online and also normal buy, cases of fraud associated with it are additionally rising. In actuality, false exchanges are scattered with veritable exchanges, and basic example for coordinating procedures is not frequently adequate to identify those frauds accurately. Usage of effective fraud recognition frameworks has in this manner gotten to be basic for all credit card distributing banks to decrease their losses. Many current systems based on artificial intelligence, Fuzzy logic, machine learning, data mining, sequence alignment, genetic programming, and so on have advanced in distinguishing different credit card fake transactions. A reasonable seeing on all these methodologies will absolutely lead to an efficient credit card fraud detection framework. This paper suggested an anomaly detection model based on a hybrid simulated annealing (SA) and back-propagation algorithm for feed-forward neural network (FFNN), which joined the significant global searching capability of SA with the precise local searching element of back-propagation FFNNs to improve theinitial weights of a neural network toward getting a better result for detection fraud.
Index Terms: Artificial Neural Network, Back-propagation, Back-propagation Feed-forward Neural Network, Feed-forward Neural Network, Simulated Annealing, Simulated Annealing-back-propagation Feed-forward Neural Network
References
[2] C. Yin, A. H. Awlla, Z. Yin and J. Wang. “Botnet detection based on genetic neural network.” International Journal of Security and Its
Applications, vol. 9, pp. 97-104, Nov. 2015.
[3] V. Van Vlasselaer, C. Bravo, O. Caelen and B. Baesens. “A novel approach for automated credit card transaction fraud detection using network-based extensions.” Decision Support Systems, vol. 75, pp. 38-48, Jul. 2015.
[4] D. Sanchez, M. A. Vila, L. Cerda and J. M. Serrano. “Association rules applied to credit card fraud detection.” Expert Systems with Applications,vol. 36, pp. 3630-3640, 2009.
[5] S. Suganya and N. Kamalraj. “A survey on credit card fraud detection.” International Journal of Computer Science and Mobile Computing, vol. 4, pp. 241-244, Nov. 2015.
[6] J. Bernal and J. Torres-Jimenez. “SAGRAD: A program for neural network training with simulated annealing and the conjugate gradient method.” Journal of Research of the National Institute of Standards and Technology, vol. 120, pp. 113-128, 2015.
[7] S. J. Subavathi and T. Kathirvalavakumar, “Adaptive modified backpropagation algorithm based on differential errors.” International Journal of Computer Science, Engineering and Applications,vol. 1, no. 5, pp. 21-33, Oct. 2011.
[8] A. T. Kalai. “Simulated annealing for convex optimization.” Mathematics of Operations Research, vol. 31, pp. 253-266, 2006.
[9] C. M. Tan, Ed. Simulated Annealing. Vienna, Austria: In-Teh is Croatian Branch of I-Tech Education and Publishing KG, Sep. 2008.
[10] S. H. Zhan, J. Lin, Z. J. Zhang and Y. W. Zhong. “List-based simulated annealing algorithm for traveling salesman problem.” Computational Intelligence and Neuroscience, vol. 2016, pp. 12, Mar. 2016.
[11] N. A. Hamid, N. M. Nawi, R. Ghazali and M. N. M. Salleh. “Solving local minima problem in back propagation algorithm using adaptive gain, adaptive momentum and adaptive learning rate on classification problems,” International Conference Mathematical and Computational Biology. Malacca, Malaysia, pp. 448-455, Apr.2011.
