پیوندهای مفید
آمار
| تعداد نشریات | 8 |
| تعداد شماره‌ها | 427 |
| تعداد مقالات | 5,557 |
| تعداد مشاهده مقاله | 6,705,419 |
| تعداد دریافت فایل اصل مقاله | 5,668,692 |
Sequential fraud detection by determining proper sequence length in payment cards using HMM | ||
| AUT Journal of Modeling and Simulation | ||
| مقاله 7، دوره 52، شماره 1، شهریور 2020، صفحه 63-76 اصل مقاله (1.28 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/miscj.2020.17052.5170 | ||
| نویسندگان | ||
| mehrdad kargari* 1؛ gazaleh shahidi2 | ||
| 1Faculty of Industrial and Systems, Industrial and Systems, Tarbiat Modares, Tehran, Iran | ||
| 2Tarbiat Modares University, Industrial and systems engineering | ||
| چکیده | ||
| The use of bank cards has increased significantly in recent years. This has resulted in increasing the probability of internet payment card frauds and has highly imposed losses on customers, institutions and banks. The methods used to detect frauds in this area mainly require a huge volume of historical data. On the other hand, these methods usually work well when there are single bank transactions, which means they only have the ability to detect frauds during single bank transactions and do not reveal fraudulent sequence identification. In this paper, a model is proposed to determine the appropriate sequence length required to evaluate every single customer's spending behavior. Through adding the feature of fraudulent sequence detection in payment cards, the proposed model has been completed. This model automatically creates and updates the Hidden Markov Model of each sequence, and ultimately detects frauds by comparing the Kullback-Leibler divergence between Hidden Markov Model of each sequence. The fraud detection is presented by real semi-supervised payment cards data of an Iranian bank. The obtained F-Score, derived from 7 real fraudulent scenarios created under the supervision of a bank expert, representing 87%. Using the proposed model also leads to a reduction in the fraudulent sequences incidence cost of 81%. | ||
| کلیدواژه‌ها | ||
| Payment card؛ Sequential spending behavior؛ Fraud detection؛ Hidden Markov Model؛ KL divergence | ||
| مراجع | ||
|
Akhilomen, John. 2013. “Data Mining Application for Cyber Credit-Card Fraud Detection System.” Pp. 218–228 in Industrial Conference on Data Mining. Springer.
Ariu, Davide, Roberto Tronci, and Giorgio Giacinto. 2011. “HMMPayl: An Intrusion Detection System Based on Hidden Markov Models.” Computers & Security 30(4):221–41.
Bang, June-ho, Young-Jong Cho, and Kyungran Kang. 2017. “Anomaly Detection of Network-Initiated LTE Signaling Traffic in Wireless Sensor and Actuator Networks Based on a Hidden Semi-Markov Model.” Computers & Security 65:108–20.
Behera, Tanmay Kumar, and Suvasini Panigrahi. 2017. “Credit Card Fraud Detection Using a Neuro-Fuzzy Expert System.” Pp. 835–843 in Computational Intelligence in Data Mining. Springer.
Bentley, Peter J., Jungwon Kim, Gil-Ho Jung, and Jong-Uk Choi. 2000. “Fuzzy Darwinian Detection of Credit Card Fraud.” in the 14th Annual Fall Symposium of the Korean Information Processing Society. Vol. 14.
Bhattacharyya, Siddhartha, Sanjeev Jha, Kurian Tharakunnel, and J. Christopher Westland. 2011. “Data Mining for Credit Card Fraud: A Comparative Study.” Decision Support Systems 50(3):602–613.
Bhusari, V., and S. Patil. 2011. “Study of Hidden Markov Model in Credit Card Fraudulent Detection.” International Journal of Computer Applications 20(5):33–36.
Brabazon, Anthony, Jane Cahill, Peter Keenan, and Daniel Walsh. 2010. “Identifying Online Credit Card Fraud Using Artificial Immune Systems.” Pp. 1–7 in Evolutionary Computation (CEC), 2010 IEEE Congress on. IEEE.
Chandola, Varun, Arindam Banerjee, and Vipin Kumar. 2012. “Anomaly Detection for Discrete Sequences: A Survey.” IEEE Transactions on Knowledge and Data Engineering 24(5):823–839.
Chen, Rong-Chang, Tung-Shou Chen, and Chih-Chiang Lin. 2006. “A New Binary Support Vector System for Increasing Detection Rate of Credit Card Fraud.” International Journal of Pattern Recognition and Artificial Intelligence 20(02):227–239.
Chen, Rong-Chang, Ming-Li Chiu, Ya-Li Huang, and Lin-Ti Chen. 2004. “Detecting Credit Card Fraud by Using Questionnaire-Responded Transaction Model Based on Support Vector Machines.” Intelligent Data Engineering and Automated Learning–IDEAL 2004 800–806.
Dheepa, V., and R. Dhanapal. 2012. “Behavior Based Credit Card Fraud Detection Using Support Vector Machines.” ICTACT Journal on Soft Computing 4(4):391–7.
Dorj, E., C. Chen, and M. Pecht. 2013. “A Bayesian Hidden Markov Model-Based Approach for Anomaly Detection in Electronic Systems.” Pp. 1–10 in. IEEE.
Dorronsoro, Jose R., Francisco Ginel, C. Sgnchez, and C. S. Cruz. 1997. “Neural Fraud Detection in Credit Card Operations.” IEEE Transactions on Neural Networks 8(4):827–834.
Duman, Ekrem, and M. Hamdi Ozcelik. 2011. “Detecting Credit Card Fraud by Genetic Algorithm and Scatter Search.” Expert Systems with Applications 38(10):13057–13063.
Epaillard, Elise, and Nizar Bouguila. 2016. “Proportional Data Modeling with Hidden Markov Models Based on Generalized Dirichlet and Beta-Liouville Mixtures Applied to Anomaly Detection in Public Areas.” Pattern Recognition 55:125–36.
Eshghi, Abdollah, and Mehrdad Kargari. 2018. “Detecting Frauds Using Customer Behavior Trend Analysis and Known Scenarios.” International Journal of Industrial Engineering & Production Research 29(1):91–101.
Eshghi, Abdollah, and Mehrdad Kargari. 2019a. “Introducing a Method for Combining Supervised and Semi-Supervised Methods in Fraud Detection.” Pp. 23–30 in 2019 15th Iran International Industrial Engineering Conference (IIIEC). IEEE.
Eshghi, Abdollah, and Mehrdad Kargari. 2019b. “Introducing a New Method for the Fusion of Fraud Evidence in Banking Transactions with Regards to Uncertainty.” Expert Systems with Applications 121:382–92.
Falaki, S. O., B. K. Alese, O. S. Adewale, J. O. Ayeni, G. A. Aderounmu, and W. O. Ismaila. 2012. “Probabilistic Credit Card Fraud Detection System in Online Transactions.” Int. J. Softw. Eng. Appl 6(4):69–78.
Forkan, Abdur Rahim Mohammad, Ibrahim Khalil, Zahir Tari, Sebti Foufou, and Abdelaziz Bouras. 2015. “A Context-Aware Approach for Long-Term Behavioural Change Detection and Abnormality Prediction in Ambient Assisted Living.” Pattern Recognition 48(3):628–41.
Fuse, T., and K. Kamiya. 2017. “Statistical Anomaly Detection in Human Dynamics Monitoring Using a Hierarchical Dirichlet Process Hidden Markov Model.” IEEE Transactions on Intelligent Transportation Systems 18(11):3083–92.
Gadi, Manoel Fernando Alonso, Xidi Wang, and Alair Pereira do Lago. 2008. “Credit Card Fraud Detection with Artificial Immune System.” Pp. 119–131 in ICARIS. Vol. 8. Springer.
Ganji, Venkata Ratnam, and Siva Naga Prasad Mannem. 2012. “Credit Card Fraud Detection Using Anti-k Nearest Neighbor Algorithm.” International Journal on Computer Science and Engineering 4(6):1035–1039.
Ghosh, Sushmito, and Douglas L. Reilly. 1994. “Credit Card Fraud Detection with a Neural-Network.” Pp. 621–630 in System Sciences, 1994. Proceedings of the Twenty-Seventh Hawaii International Conference on. Vol. 3. IEEE.
Guo, Tao, and Gui-Yang Li. 2008. “Neural Data Mining for Credit Card Fraud Detection.” Pp. 3630–3634 in Machine Learning and Cybernetics, 2008 International Conference on. Vol. 7. IEEE.
Halvaiee, Neda Soltani, and Mohammad Kazem Akbari. 2014. “A Novel Model for Credit Card Fraud Detection Using Artificial Immune Systems.” Applied Soft Computing 24:40–49.
Hershey, John R., Peder A. Olsen, and Steven J. Rennie. 2007. “Variational Kullback-Leibler Divergence for Hidden Markov Models.” Pp. 323–328 in Automatic Speech Recognition & Understanding, 2007. ASRU. IEEE Workshop on. IEEE.
Kokkinaki, Angelika I. 1997. “On Atypical Database Transactions: Identification of Probable Frauds Using Machine Learning for User Profiling.” Pp. 107–113 in Knowledge and Data Engineering Exchange Workshop, 1997. Proceedings. IEEE.
Kullback, Solomon. 1997. Information Theory and Statistics. Courier Corporation.
Kumar, Sandeep, and Eugene H. Spafford. 1994. “A Pattern Matching Model for Misuse Intrusion Detection.”
Kumari, Nitu, S. Kannan, and A. Muthukumaravel. 2014. “Credit Card Fraud Detection Using Hidden Markov Model-a Survey.” Middle-East Journal of Scientific Research 19(6):821–825.
Lu, Qibei, and Chunhua Ju. 2011. “Research on Credit Card Fraud Detection Model Based on Class Weighted Support Vector Machine.” Journal of Convergence Information Technology 6(1).
Maes, Sam, Karl Tuyls, Bram Vanschoenwinkel, and Bernard Manderick. 2002. “Credit Card Fraud Detection Using Bayesian and Neural Networks.” Pp. 261–270 in Proceedings of the 1st international naiso congress on neuro fuzzy technologies.
Malini, N., and M. Pushpa. 2017. “Analysis on Credit Card Fraud Identification Techniques Based on KNN and Outlier Detection.” Pp. 255–258 in Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB), 2017 Third International Conference on. IEEE.
Manikandan, S. 2010. “Data Transformation.” Journal of Pharmacology and Pharmacotherapeutics 1(2):126.
Mule, Komal, and Madhuri Kulkarni. 2014. “Credit Card Fraud Detection Using Hidden Markov Model (HMM).”
Nilson Report, 2016.
Patidar, Raghavendra, Lokesh Sharma, and others. 2011. “Credit Card Fraud Detection Using Neural Network.” International Journal of Soft Computing and Engineering (IJSCE) 1(32–38).
Quah, Jon TS, and M. Sriganesh. 2008. “Real-Time Credit Card Fraud Detection Using Computational Intelligence.” Expert Systems with Applications 35(4):1721–1732.
Rabiner, Lawrence R. 1989. “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition.” Proceedings of the IEEE 77(2):257–286.
Rabiner, Lawrence R., and Biing-Hwang Juang. 1986. “An Introduction to Hidden Markov Models.” Ieee Assp Magazine 3(1):4–16.
Robinson, William N., and Andrea Aria. 2018. “Sequential Fraud Detection for Prepaid Cards Using Hidden Markov Model Divergence.” Expert Systems With Applications 91:235–251.
Sahin, Yusuf, Serol Bulkan, and Ekrem Duman. 2013. “A Cost-Sensitive Decision Tree Approach for Fraud Detection.” Expert Systems with Applications 40(15):5916–5923.
Şahin, Yusuf G., and Ekrem Duman. 2011. “Detecting Credit Card Fraud by Decision Trees and Support Vector Machines.”
Save, Prajal, Pranali Tiwarekar, Ketan N. Jain, and Neha Mahyavanshi. 2017. “A Novel Idea for Credit Card Fraud Detection Using Decision Tree.” International Journal of Computer Applications 161(13).
Srivastava, A., A. Kundu, S. Sural, and A. Majumdar. 2008a. “Credit Card Fraud Detection Using Hidden Markov Model.” IEEE Transactions on Dependable and Secure Computing 5(1):37–48.
Srivastava, A., A. Kundu, S. Sural, and A. Majumdar. 2008b. “Credit Card Fraud Detection Using Hidden Markov Model.” IEEE Transactions on Dependable and Secure Computing 5(1):37–48.
Srivastava, Abhinav, Amlan Kundu, Shamik Sural, and Arun Majumdar. 2008. “Credit Card Fraud Detection Using Hidden Markov Model.” IEEE Transactions on Dependable and Secure Computing 5(1):37–48.
Syeda, Mubeena, Yan-Qing Zhang, and Yi Pan. 2002. “Parallel Granular Neural Networks for Fast Credit Card Fraud Detection.” Pp. 572–577 in Fuzzy Systems, 2002. FUZZ-IEEE’02. Proceedings of the 2002 IEEE International Conference on. Vol. 1. IEEE.
Van Vlasselaer, Véronique, Cristián Bravo, Olivier Caelen, Tina Eliassi-Rad, Leman Akoglu, Monique Snoeck, and Bart Baesens. 2015. “APATE: A Novel Approach for Automated Credit Card Transaction Fraud Detection Using Network-Based Extensions.” Decision Support Systems 75:38–48.
Vosough, Maliheh, Mohammad Taghi Taghavi Fard, and Mahmoud Alborzi. 2015. “Bank Card Fraud Detection Using Artificial Neural Network.” Journal of Information Technology Management 6(4):721–746.
Warrender, Christina, Stephanie Forrest, and Barak A. Pearlmutter. 1999. “Detecting Intrusions Using System Calls: Alternative Data Models.”
Wiese, Bénard, and Christian Omlin. 2009. “Credit Card Transactions, Fraud Detection, and Machine Learning: Modelling Time with LSTM Recurrent Neural Networks.” Innovations in Neural Information Paradigms and Applications 231–268.
Wong, Nicholas, Pradeep Ray, Greg Stephens, and Lundy Lewis. 2012. “Artificial Immune Systems for the Detection of Credit Card Fraud: An Architecture, Prototype and Preliminary Results.” Information Systems Journal 22(1):53–76.
Wu, Chih-Hung, Gwo-Hshiung Tzeng, Yeong-Jia Goo, and Wen-Chang Fang. 2007. “A Real-Valued Genetic Algorithm to Optimize the Parameters of Support Vector Machine for Predicting Bankruptcy.” Expert Systems with Applications 32(2):397–408.
Xie, Y., and S. Z. Yu. 2009. “A Large-Scale Hidden Semi-Markov Model for Anomaly Detection on User Browsing Behaviors.” IEEE/ACM Transactions on Networking 17(1):54–65.
Zareapoor, Masoumeh, and Pourya Shamsolmoali. 2015. “Application of Credit Card Fraud Detection: Based on Bagging Ensemble Classifier.” Procedia Computer Science 48:679–685.
Zaslavsky, Vladimir, and Anna Strizhak. 2006. “Credit Card Fraud Detection Using Self-Organizing Maps.” Information and Security 18:48.
| ||
|
آمار تعداد مشاهده مقاله: 399 تعداد دریافت فایل اصل مقاله: 305 |
||