Abstract
For over 15 years auction services have grown rapidly, constituting a major part of e-commerce worldwide. Unfortunately, they also provide opportunities for criminals to distribute illicit goods, launder money or commit other types of fraud. This calls for methods to mitigate this threat. The following paper discusses the methods of identifying the accounts of users participating in internet auctions that have been hijacked (taken over) by malicious individuals and utilised for fraudulent purposes. Two primary methods are described, monitoring users’ activities (e.g. the number of auctions created over time) with EWMA and clustering similar auction categories into groups for the purpose of assessing users’ sellers profiles and detecting their sudden changes. These methods, utilised together allow for real-time detection of suspicious accounts. The proposed models are validated on real data gathered from an auction web site.
This work is partially supported by NCBiR grant 0021/R/ID2.
Chapter PDF
Similar content being viewed by others
References
Alpert, C., Kahng, A., Yao, Z.: Spectral partitioning: the more eigenvectors the better. Discrete Applied Mathematics 90, 3–26 (1999)
Beranek, L.: Auditing Electronic Auctions Systems. ISACA OnLine Journal 4 (2010), http://www.isaca.org/Journal/Past-Issues/2010/Volume-4/Pages/default.aspx
Boyd, C., Mao, W.: Security Issues for Electronic Auctions. Technical Report, Hewlett Packard (2000)
Chang, J.S., Chang, W.H.: An Early Fraud Detection Mechanism for Online Auctions Based on Phased Modeling. In: Proceedings of Joint Conferences on Pervasive Computing (JCPC), Taipei, pp. 743–748 (2009)
Chau, D., Faloutsos, C.: Fraud Detection in Electronic Auction. In: Proceedings of EWMF 2005: European Web Mining Forum, Porto (2005)
Cheng, D., et al.: On a recursive spectral algorithm for clusterin from pairwise similarities. MIT LCS Technical Report MIT-LCS-TR-906 (2003)
Chua, C., Wareham, J.: Fighting Internet Auction Fraud: An assessment and proposal. IEEE Computer 37(10), 31–37 (2004)
Dhillon, I.: Co-clustering documents and words using bipartite spectral graph partitioning. In: Knowledge Discovery and Data Mining, pp. 269–274 (2001)
Dong, F., Shatz, S., Zu, H.: Combating Online in-Auction Fraud: Clues, Techniques and Challenges. Computer Science Review 3(4), 245–258 (2009)
Fowlkes, C., et al.: Spectral Grouping Using the Nyström Method. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 214–225 (2004)
Gavish, B., Tucci, C.: Reducing Internet Auction Fraud. Communications of the ACM 51(5), 89–97 (2008)
Kannan, R., Vempala, S., Vetta, A.: On clusterings: good, bad and spectral. In: Proceedings of the 41st Annual Symposium on Foundations of Computer Science, California, pp. 367–380. IEEE Computer Society (2000)
Kruegel, C., Vigna, G., Robertson, W.: A multi-model approach to the detection of web-based attacks. Computer Networks 48, 717–738 (2005)
Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics Doklady 10, 707–710 (1966)
Pałka, D., Zachara, M.: Learning Web Application Firewall - Benefits and Caveats. In: Tjoa, A.M., Quirchmayr, G., You, I., Xu, L., et al. (eds.) ARES 2011. LNCS, vol. 6908, pp. 295–308. Springer, Heidelberg (2011)
Pietro, R., Mancini, L. (eds.): Intrusion Detection Systems. Springer (2008) ISBN: 978-0-387-77265-3
Putting an End to Account-Hijacking Identity Theft. Federal Deposit Insurance Corporation (2004)
Reichling, F.: Effects of Reputation Mechanisms on Fraud Prevention in eBay Auctions. Thesis, Stanford University (2004)
Quaterly Retail E-commerce Sales (2009), http://www.census.gov/retail/mrts/www/data/pdf/09Q4.pdf
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)
The risk of criminal exploitation of online auctions. Australian Institute of Criminology (2007)
Weiss, Y.: Segmentation using eigenvectors: a unifying view. In: Proceedings of IEEE International Conference on Computer Vision, pp. 975–982 (1999)
Wheeler, R., Aitken, S.: Multiple algorithms for fraud detection. Knowledge-Based Systems 13, 93–99 (2000)
Xiang, T., Gong, S.: Spectral clustering with eigenvector selection. Pattern Recognition 41(3), 1012–1029 (2008)
Zhang, B., Zhou, Y., Faloutos, C.: Toward a Comprehensive Model in Internet Auction Fraud Detection. In: Proceedings of Hawaii International Conference on System Sciences, pp. 79–87. IEEE Computer Society (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 IFIP International Federation for Information Processing
About this paper
Cite this paper
Zachara, M., Pałka, D. (2012). Detecting Unusual User Behaviour to Identify Hijacked Internet Auctions Accounts. In: Quirchmayr, G., Basl, J., You, I., Xu, L., Weippl, E. (eds) Multidisciplinary Research and Practice for Information Systems. CD-ARES 2012. Lecture Notes in Computer Science, vol 7465. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32498-7_41
Download citation
DOI: https://doi.org/10.1007/978-3-642-32498-7_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32497-0
Online ISBN: 978-3-642-32498-7
eBook Packages: Computer ScienceComputer Science (R0)