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A Supervised Auto-Tuning Approach for a Banking Fraud Detection System

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Cyber Security Cryptography and Machine Learning (CSCML 2017)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 10332))

Abstract

In this paper, we propose an extension to Banksealer, one of the most recent and effective banking fraud detection systems. In particular, until now Banksealer was unable to exploit analyst feedback to self-tune and improve its performance. It also depended on a complex set of parameters that had to be tuned by hand before operations.

To overcome both these limitations, we propose a supervised evolutionary wrapper approach, that considers analyst’s feedbacks on fraudulent transactions to automatically tune feature weighting and improve Banksealer’s detection performance. We do so by means of a multi-objective genetic algorithm.

We deployed our solution in a real-world setting of a large national banking group and conducted an in-depth experimental evaluation. We show that the proposed system was able to detect sophisticated frauds, improving Banksealer’s performance of up to 35% in some cases.

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References

  1. Kaspersky Security Bulletin 2016. Technical report, Kaspersky Lab (2017). https://goo.gl/Jzkab2

  2. Almuallim, H., Dietterich, T.G.: Learning with many irrelevant features. In: AAAI, vol. 91, pp. 547–552. Citeseer (1991)

    Google Scholar 

  3. Amer, M., Goldstein, M.: Nearest-neighbor and clustering based anomaly detection algorithms for RapidMiner. In: Proceedings of the 3rd RapidMiner Community Meeting and Conference (RCOMM 2012), pp. 1–12 (2012)

    Google Scholar 

  4. Bolton, R.J., Hand, D.J.: Statistical fraud detection: a review. Stat. Sci. 17 (2002)

    Google Scholar 

  5. Bolton, R.J., Hand, D.J., David J.H.: Unsupervised profiling methods for fraud detection. In: Proceedings of Credit Scoring and Credit Control VII, pp. 5–7 (2001)

    Google Scholar 

  6. Cardie, C.: Using decision trees to improve case-based learning. In: Proceedings of the Tenth International Conference on Machine Learning, pp. 25–32 (1993)

    Google Scholar 

  7. Carminati, M., Caron, R., Maggi, F., Epifani, I., Zanero, S.: BankSealer: an online banking fraud analysis and decision support system. In: ICT Systems Security and Privacy Protection. IFIP Advances in Information and Communication Technology, vol. 428, pp. 380–394. Springer, Heidelberg (2014)

    Google Scholar 

  8. Carminati, M., Caron, R., Maggi, F., Epifani, I., Zanero, S.: BankSealer: a decision support system for online banking fraud analysis and investigation. Comput. Secur. 53, 175–186 (2015) http://dx.doi.org/10.1016/j.cose.2015.04.002

  9. Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 15:1–15:58 (2009)

    Google Scholar 

  10. Cost, S., Salzberg, S.: A weighted nearest neighbor algorithm for learning with symbolic features. Mach. Learn. 10(1), 57–78 (1993)

    Google Scholar 

  11. Deb, K., Agrawal, R.B.: Simulated binary crossover for continuous search space. Complex Syst. 9(3), 1–15 (1994)

    MathSciNet  MATH  Google Scholar 

  12. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  13. Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer Science & Business Media, New York (2003)

    Google Scholar 

  14. Goldstein, M., Dengel, A.: Histogram-Based Outlier Score (HBOS): a fast unsupervised anomaly detection algorithm. In: KI-2012: Poster and Demo Track, pp. 59–63 (2012)

    Google Scholar 

  15. Han, J., Kamber, M.: Data Mining: Concepts and Techniques. The Morgan Kaufmann Series in Data Management Systems Series. Elsevier Science & Tech, Amsterdam (2006)

    MATH  Google Scholar 

  16. Jirapech-Umpai, T., Aitken, S.: Feature selection and classification for microarray data analysis: evolutionary methods for identifying predictive genes. BMC Bioinform. 6(1), 148 (2005)

    Article  Google Scholar 

  17. Kira, K., Rendell, L.A.: A practical approach to feature selection. In: Proceedings of the Ninth International Workshop on Machine Learning, pp. 249–256 (1992)

    Google Scholar 

  18. Kohavi, R., John, G.H.: Automatic parameter selection by minimizing estimated error. In: ICML, pp. 304–312. Citeseer (1995)

    Google Scholar 

  19. Kohavi, R., John, G.H.: The wrapper approach. In: Feature Extraction, Construction and Selection, pp. 33–50. Springer (1998)

    Google Scholar 

  20. Kononenko, I.: Estimating attributes: analysis and extensions of RELIEF. In: Bergadano, F., Raedt, L. (eds.) ECML 1994. LNCS, vol. 784, pp. 171–182. Springer, Heidelberg (1994). doi:10.1007/3-540-57868-4_57

    Chapter  Google Scholar 

  21. Langley, P., Sage, S.: Induction of selective Bayesian classifiers. In: Proceedings of the Tenth International Conference on Uncertainty in Artificial Intelligence, pp. 399–406. Morgan Kaufmann Publishers Inc. (1994)

    Google Scholar 

  22. Mahalanobis, P.C.: On the generalized distance in statistics. In: Proceedings of the National Institute of Science of India, vol. 2, pp. 49–55 (1936)

    Google Scholar 

  23. Miller, B.L., Goldberg, D.E.: Genetic algorithms, tournament selection, and the effects of noise. Complex Syst. 9(3), 193–212 (1995)

    MathSciNet  Google Scholar 

  24. Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1998)

    Google Scholar 

  25. Mitchell, T.: Machine Learning. McGraw Hill, New York (1997)

    Google Scholar 

  26. Mitra, P., Murthy, C., Pal, S.K.: Unsupervised feature selection using feature similarity. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 301–312 (2002)

    Article  Google Scholar 

  27. Obayashi, S., Takahashi, S., Takeguchi, Y.: Niching and elitist models for MOGAs. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 260–269. Springer, Heidelberg (1998). doi:10.1007/BFb0056869

    Chapter  Google Scholar 

  28. Oliveira, L.S., Sabourin, R., Bortolozzi, F., Suen, C.Y.: Feature selection using multi-objective genetic algorithms for handwritten digit recognition. In: Proceedings of 16th International Conference on Pattern Recognition, vol. 1, pp. 568–571. IEEE (2002)

    Google Scholar 

  29. Parks, G.T., Miller, I.: Selective breeding in a multiobjective genetic algorithm. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 250–259. Springer, Heidelberg (1998). doi:10.1007/BFb0056868

    Chapter  Google Scholar 

  30. Parzen, E.: On estimation of a probability density function and mode. Ann. Math. Stat. 33(3), 1065–1076 (1962)

    Google Scholar 

  31. Phua, C., Alahakoon, D., Lee, V.: Minority report in fraud detection: classification of skewed data. SIGKDD Explor. Newsl. 6(1), 50–59 (2004)

    Google Scholar 

  32. Phuong, T.M., Lin, Z., Altman, R.B.: Choosing SNPs using feature selection. In: Proceedings of Computational Systems Bioinformatics Conference, 2005, pp. 301–309. IEEE (2005)

    Google Scholar 

  33. Punch III, W.F., Goodman, E.D., Pei, M., Chia-Shun, L., Hovland, P.D., Enbody, R.J.: Further research on feature selection and classification using genetic algorithms. In: ICGA, pp. 557–564 (1993)

    Google Scholar 

  34. Rudolph, G.: Evolutionary search under partially ordered sets. Dept. Comput. Sci./LS11, Univ. Dortmund, Dortmund, Germany, Technical report CI-67/99 (1999)

    Google Scholar 

  35. Soyel, H., Tekguc, U., Demirel, H.: Application of NSGA-II to feature selection for facial expression recognition. Comput. Electr. Eng. 37(6), 1232–1240 (2011)

    Article  Google Scholar 

  36. Wei, W., Li, J., Cao, L., Ou, Y., Chen, J.: Effective detection of sophisticated online banking fraud on extremely imbalanced data. World Wide Web 16(4), 449–475 (2013). http://dx.doi.org/10.1007/s11280-012-0178-0

  37. Wettschereck, D., Aha, D.W., Mohri, T.: A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms. Artif. Intell. Rev. 11(1–5), 273–314 (1997)

    Article  Google Scholar 

  38. Yang, J., Honavar, V.: Feature subset selection using a genetic algorithm. In: Liu, H., Motoda, H. (eds.) Feature Extraction, Construction and Selection, pp. 117–136. Springer, New York (1998)

    Chapter  Google Scholar 

  39. Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: empirical results. Evol. Comput. 8(2), 173–195 (2000)

    Article  Google Scholar 

Download references

Acknowledgment

This work has received funding from the European Union’s Horizon 2020 Programme, under grant agreement 700326 “RAMSES”, as well as from projects co-funded by the Lombardy region and Secure Network S.r.l.

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Authors and Affiliations

  1. Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy

    Michele Carminati, Luca Valentini & Stefano Zanero

Authors
  1. Michele Carminati
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  2. Luca Valentini
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  3. Stefano Zanero
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Corresponding author

Correspondence to Michele Carminati .

Editor information

Editors and Affiliations

  1. Ben-Gurion University of the Negev , Beer-Sheva, Israel

    Shlomi Dolev

  2. Tata Consultancy Services (India) , Chennai, Tamil Nadu, India

    Sachin Lodha

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Carminati, M., Valentini, L., Zanero, S. (2017). A Supervised Auto-Tuning Approach for a Banking Fraud Detection System. In: Dolev, S., Lodha, S. (eds) Cyber Security Cryptography and Machine Learning. CSCML 2017. Lecture Notes in Computer Science(), vol 10332. Springer, Cham. https://doi.org/10.1007/978-3-319-60080-2_17

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  • DOI: https://doi.org/10.1007/978-3-319-60080-2_17

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