# A Secured Approach to Credit Card Fraud Detection Using Hidden Markov Model

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3 Figure 1: Layer of Neural Network in Credit Card [3] The neural network produces output in real value between 0 to 1. If the neural network produce output below.7 then the transaction is ok and if it produce a output above.7 then the change of transaction being illegal increases. Advantage: It detects the transaction at the time when transaction is in Disadvantage: 1) Number of parameters to be set before training begins. There are no clear rules to set these parameters. 2) Network differ in the way their neurons are interconnected i.e. topology of a network has a large influence on the performance of network and so far there is no method that determine optimal topology for a given problem. IV. COMPARISION OF VARIOUS TECHNIQUES Fraud Detection Techniques Logistic Regression Advantage It produces a simple probability formula for classification. It works well with linear data for credit card fraud detection. Disadvantage 1) It does not deal with non linear data for credit card fraud detection. 2) It is not capable to detect the fraud at a time when transaction is in Decision Tree It can handle non linear and interactive effects of input variables. K-Nearest It does not require Neighbour establishing any Algorithm predictive model before classification Naïve Bayes It only provides a Algorithm theoretical justification to the fact but does not use bayes theorem Artificial It detects the Neural Network transaction at the time when transaction is in Hidden 1) Hidden Markov Markov Model 1) It has complex algorithm. Even a small change in observed data might change the structure of a tree. Choosing splitting criteria is also difficult. 2) It is not capable to detect the fraud at a time when transaction is in 1) Accuracy is highly dependent on the measure of distance. 2) It is not capable to detect the fraud at a time when transaction is in 1) In real practice the dependences exists between the variable. 2) It is not capable to detect the fraud at a time when transaction is in 1) Number of parameters to be set before training begins. There are no clear rules to set these parameters. 2) Network differ in the way their neurons are interconnected and so far there is no method that determine optimal topology for a given problem. 1) It detects the fraud only after some ISSN: All Rights Reserved 2014 IJARCET 1578

4 Model. (HMM) is capable detect transaction in to the is 2) The main feature of the HMM-based model reducing False (FP) is in Positive transactions predict as fraud by a fraud detection system even though they are really customer. genuine transactions so it is not secured for initial transactions. some As shown in figure 2 there are two phases of HMM. In training phase card holder transaction amount is converted in observation symbols i.e. low medium or high and form sequences from them. After the sequence is formed threshold is calculated from the sequence of amount. In the detection phase client enters amount and form an initial sequence of symbol. Let O 1, O 2, O 3.. O R be such sequence of length R up to time t. This sequence is the input to HMM and from that we compute the threshold of acceptance α 1. Let O R+1 be a symbol of new transaction at time t+1.now with new transaction we generate a new sequence O 2, O 3,.. O R,O R+1. We input these sequence in HMM and calculate the new threshold of acceptance if amount is less than threshold than amount is added in new sequence else the it is detected as anomaly VI. HMAC ONE TIME PASSWORD (HOTP) HMAC is hash message authentication code. HOTP is used for generating an 8 digit one time password for security. An important steps to obtain HOTP is describe in Figure3. V. HIDDEN MARKOV MODEL In HMM considered three price ranges such as low (l), medium (m) and high (h). First we need to find out transaction amount to a particular group either it will be in low, medium or high [1]. For example let l= (0, \$150) m= (\$150, \$ 250) and h= (\$250, credit card limit). If transaction of card holder is \$70 then price range or observation symbol is low. Figure 3: HMAC one time password [8] Figure 2: Process flow of credit card fraud detection system [1]. ISSN: All Rights Reserved 2014 IJARCET 1579

5 As shown in figure step1 is generating HMAC-SHA1 value. Let HS= HMAC SHA1 (K, C) [9] where K is secret key and C is a counter. Step2 and step 3 is generating 4 bytes string i.e. dynamic truncation. Lets understand that by taking example [9] Byte Number Byte Value 1f e 02 ca ef 7f 19 da 8e 94 5b 55 5a ********* As shown in example the last byte has hex value 5a which means the value of lower four byte is 10 (a). Now the value of four byte starting at byte 10 is 50ef7f19. Now we will convert 50ef7f19 into number. 5 16⁷+0 16⁶+14 16⁵+15 16⁴+7 16³+15 16²+1 16¹+9 16⁰ = Step4 is to get 8 digit numbers by performing modulo. HOTP-Value = HOTP (K, C) mod 10 d [9] where d is desired numbers of digit. For example mod 10^8= thus is 8 digit HOTP. This is how HMAC one time password is generated and send to mobile as sms for security. VII. PROPOSED WORK In the proposed work HMM is used along with HOTP to make HMM more secured as we have seen above HMM needs training and during training some transactions are involved and fraud is not detected during training but it is detected after training so HOTP is used for secured approach in HMM so make initial transaction secure by sending one time password i.e. security code to clients mobile if the security code entered by client is correct then only transaction is done successfully else transaction is not allowed to But once the HMM is trained and ready for detection client does not need to enter any security code unless HMM detects the transaction is above threshold value. If the transaction is above threshold value security code is send to mobile and client need to enter that security code then only transaction is done successfully else transaction is not allowed to Figure 4: Proposed model during training. As shown in figure 4 here client has to enter a valid credit card number then a security code i.e. HOTP will be generated and sent to mobile as sms client has to enter that security code and amount if it is valid then only transaction is done successfully. During training of HMM the client has to enter the security code every time then only transaction can be done. Figure 5: Proposed model of credit card fraud detection after training during detection ISSN: All Rights Reserved 2014 IJARCET 1580

6 As shown in figure 5 after training the client need to enter credit card number and amount if HMM detects that amount is less than threshold value then transaction is done successfully else if amount is more than threshold value HOTP is generated and send to clients mobile. Client has to enter that security code if it is valid than only transaction is successfully else transaction is not able to progress and transaction cannot be done. After training client do not have to enter the security code every time only when amount is more than threshold value client need to enter security code generated for the security reason. Figure 7: HOTP generated and send as SMS Results during the training are as shown in Figure 8 during training clients need to enter a valid credit card number and secured code which is send to mobile as sms for successfully transaction. Figure 6: Hidden markov model along with HOTP during detection During detection as shown in Figure 6the client enters the amount which is observation symbol whether it is low, medium or high then that amount is added in the sequence and both the sequence is accepted and new threshold value is calculated and amount is tested if it is above threshold value then the HOTP is generated and send to mobile as sms and then if client enters the valid HOTP amount is added in sequence else if amount is below threshold value then the transaction is done without need of HOTP to be entered by the client. VIII. RESULT AND ANALYSIS Result for HOTP generated and send to mobile as sms are shown in figure 7 below. HOTP is 8 digit one time password which is generated as shown in figure. Figure 8: Result during training Results after training are shown in figure below where client don t have to enter the secured code every time client can do transaction if HMM detects if amount is less than threshold else the HOTP is generated and send to mobile which client need to enter in secured code fields for security reason as shown above in figure. Figure 9: Result during detection ISSN: All Rights Reserved 2014 IJARCET 1581

7 For analysis of clients amount the cluster is formed which shows the no of transaction and the amount that falls in low medium or high. Figure 10: Cluster of client amount Figure 12: FT and TP transactions As shown in figure 12 analyses show False positive and true positive transaction detected by HMM. Once HMM detects the false positive transaction HOTP is generated and send to clients mobile if client enter that correct HOTP then only transaction is done else client cannot proceed. Thus by using HMM with HOTP we get more security. IX. CONCLUSION In this paper we have brief discussion on credit card fraud detection using Hidden Markov Model. Here we have shown how the HMM can detect whether an incoming transaction is fraud or genuine. HOTP is use to generate 8 digit unique security code. In proposed model we have use HMM with HOTP to provide more security and to reduce the fraud. REFERENCES Figure 11: Different transactions of clients As shown in figure11 shows the number of transaction of different clients red color is high transaction, blue is medium transactions and pink in low transaction based on these transaction the threshold is decided. [1] Abhinav Srivastava, Amlan Kundu, Shamik Sural and Arun K. Majumdar, et al. Credit Card Fraud Detection Using Hidden Markov Model, IEEE transactions on dependable and secure computing, vol. 5, no. 1, January-march [2] S. Esakkiraj and S. Chidambaram, et al. A Predictive Approach for Fraud Detection Using Hidden Markov Model, International Journal of Engineering Research & Technology (IJERT), Vol 2, January 2013 [3] Raghavendra Patidar and Lokesh Sharma, et al. Credit Card Fraud Detection Using Neural Network International Journal of Soft Computing and Engineering (IJSCE) ISSN: , Volume-1, Issue-NCAI2011, June 2011 [4] Anshul Singh and Devesh Narayan, et al. A Survey on Hidden Markov Model for Credit Card Fraud Detection International Journal of Engineering and Advanced Technology (IJEAT) ISSN: , Volume-1, Issue-3, February 2012 ISSN: All Rights Reserved 2014 IJARCET 1582

8 [5] V.Dheepa and Dr. R.Dhanapal, et al. Analysis of Credit Card Fraud Detection Methods, International Journal of Recent Trends in Engineering, Vol 2, No. 3, November 2009 [6] Siva Parvatni Nelluri, Shaik Nagul, Dr. M.Kishore Kumar, et al Credit card fraud detection using Hidden Markov Model (HMM), International Journal of Engineering Research and technology(ijert) ISSN: ,Vol. 1 Issue 5, July 2012 [7] I-Cheng Yeh and Che-hui Lien, et al. The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients, Expert Systems with Applications [8] Hamdane, Balkis, et al. "Using the HMAC-Based One-Time Password Algorithm for TLS Authentication." Network and Information Systems Security (SAR-SSI), 2011 Conference on. IEEE, [9] D. M Raihi, M. Bellare, F. Hoornaert, and D. Naccache, et al Hotp: An hmacbased one-time password algorithm, RFC 4226, Dec Twinkle Patel Master in computer Engineering, L J Institute of Engineering and Technology Ahmedabad, Gujarat, India Ms. Ompriya Kale M.Tech (CSE), Assistant Professor, L J Institute of Engineering and Technology Ahmedabad, Gujarat, India ISSN: All Rights Reserved 2014 IJARCET 1583

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