An online auction network (OAN) is a community of users who buy or sell items through an auction site. Along with the growing popularity of auction sites, concerns about auction frauds and criminal activities have increased. As a result, fraud detection in OANs has attracted renewed interest from researchers. Since most real OANs are large-scale networks, detecting fraudulent users is usually difficult, especially when multiple users collude with each other and new online auctions are continuously added. Although collusive auction frauds are not as popular as other types of auction frauds, they are more horrible and catastrophic because they often bring huge financial losses. To tackle this issue, some techniques have been proposed to detect collusive frauds in OANs. While all of the techniques have demonstrated promising results, they often suffer from low detection performance or slow convergence, especially in large-scale OANs. In this paper, we overcome these deficiencies by presenting ICAFD, a novel technique that recasts the problem of detecting collusive frauds in large-scale OANs as an incremental semi-supervised anomaly detection problem. In this technique, we propagate reputations from a small set of labeled benign users to unlabeled users along the auction relationships between them and then incrementally update reputations when a new auction gets added to the OAN. This increases the convergence of ICAFD and allows it to avoid wasteful recalculation of reputations from scratch. Our experimental results show that ICAFD can successfully detect different types of collusive auction frauds in a reasonable detection time.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Aleem A, Antwi-Boasiako A (2011) Internet auction fraud: the evolving nature of online auctions criminality and the mitigating framework to address the threat. Int J Law Crime Justice 39(3):140–160. https://doi.org/10.1016/j.ijlcj.2011.05.003
Almendra V (2013) Finding the needle: a risk-based ranking of product listings at online auction sites for non-delivery fraud prediction. Expert Syst Appl 40(12):4805–4811. https://doi.org/10.1016/j.eswa.2013.02.027
Bangcharoensap P, Kobayashi H, Shimizu N, Yamauchi S, Murata T (2015) Two step graph-based semi-supervised learning for online auction fraud detection. In: Bifet A, May M, Zadrozny B, Gavalda R, Pedreschi D, Bonchi F, Cardoso J, Spiliopoulou M (eds) Machine learning and knowledge discovery in databases, LNAI. Springer International Publishing, Cham, pp 165–179. https://doi.org/10.1007/978-3-319-23461-8_11
Bounsiar A, Madden MG (2014) Kernels for one-class support vector machines. In: Proceedings of the 2014 International Conference on Information Science and Applications (ICISA), pp 1–4. IEEE, Piscataway. https://doi.org/10.1109/ICISA.2014.6847419
Center ICC (2015) 2015 IC3 annual report. https://pdf.ic3.gov/2015_IC3Report.pdf
Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):27. https://doi.org/10.1145/1961189.1961199
Chang WH, Chang JS (2012) An effective early fraud detection method for online auctions. Electron Commer Res Appl 11(4):346–360. https://doi.org/10.1016/j.elerap.2012.02.005
Chau DH, Pandit S, Faloutsos C (2006) Detecting fraudulent personalities in networks of online auctioneers. In: Fürnkranz J, Scheffer T, Spiliopoulou M (eds) Knowledge discovery in databases, LNCS. Springer, Berlin, pp 103–114. https://doi.org/10.1007/11871637_14
Cheng CB, Shih HS, Lee ES (2019) Auction mechanisms for solving multi-level programming. In: Fuzzy and multi-level decision making: soft computing approaches, STUDFUZZ. Springer International Publishing, Cham, pp 147–169. https://doi.org/10.1007/978-3-319-92525-7_7
Dorri A, Abadi M, Dadfarnia M (2018) SocialBotHunter: botnet detection in Twitter-like social networking services using semi-supervised collective classification. In: Proceedings of the 2018 IEEE 16th international conference on Dependable, Autonomic and Secure Computing (DASC). IEEE Computer Society, Washington, pp 496–503. https://doi.org/10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00097
Flax MM (2015) Economic crimes. LawTech Publishing Group, San Clemente
Habibollahi N, Abadi M, Dadfarnia M (2017) CAFD: detecting collusive frauds in online auction networks by combining one-class classification and collective classification. In: Proceedings of the 2017 14th international ISC conference on Information Security and Cryptology (ISCISC). IEEE, Piscataway, pp 48–53. https://doi.org/10.1109/ISCISC.2017.8488364
Howlader J, Mal AK (2015) Sealed-bid auction: a cryptographic solution to bid-rigging attack in the collusive environment. Secur Commun Netw 8(18):3415–3440. https://doi.org/10.1002/sec.1268
Ihler AT, Fischer JW, Willsky AS (2005) Loopy belief propagation: convergence and effects of message errors. J Mach Learn Res 6:905–936
Jha V, Ramu S, Shenoy PD, Venugopal KR (2017) Reputation systems: evaluating reputation among all good sellers. Data-Enabled Discov Appl 1–8. https://doi.org/10.1007/s41688-017-0008-8
Li SZ (1995) Markov random field modeling in computer vision. Springer, Berlin. https://doi.org/10.1007/978-4-431-66933-3
Lin JL, Khomnotai L (2014) Using neighbor diversity to detect fraudsters in online auctions. Entropy 16(5):2629–2641. https://doi.org/10.3390/e16052629
Liu X, Datta A, Fang H, Zhang J (2012) Detecting imprudence of ‘reliable’ sellers in online auction sites. In: Proceedings of the 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), pp 246–253. IEEE Computer Society, Washington. https://doi.org/10.1109/TrustCom.2012.123
Luo S, Wan S (2019) Leveraging product characteristics for online collusive detection in big data transactions. IEEE Access 7:40154–40164. https://doi.org/10.1109/ACCESS.2019.2891907
Machhi S, Jethava GB (2016) Feedback based trust management for cloud environment. In: Proceedings of the 2nd International Conference on Information and Communication Technology for Competitive Strategies (ICTCS), pp 114:1–114:5. ACM, New York. https://doi.org/10.1145/2905055.2905330
Majadi N, Trevathan J, Bergmann N (2018) Real-time collusive shill bidding detection in online auctions. In: Mitrovic T, Xue B, Li X (eds) Advances in artificial intelligence, LNCS. Springer International Publishing, Cham, pp 184–192. https://doi.org/10.1007/978-3-030-03991-2_19
Majadi N, Trevathan J, Bergmann N (2019) Collusive shill bidding detection in online auctions using Markov random field. Electron Commer Res Appl 34:1–13. https://doi.org/10.1016/j.elerap.2019.100831
Majadi N, Trevathan J, Gray H, Estivill-Castro V, Bergmann N (2017) Real-time detection of shill bidding in online auctions: a literature review. Comput Sci Rev 25:1–18. https://doi.org/10.1016/j.cosrev.2017.05.001
Maranzato R, Pereira A, Neubert M, do Lago AP (2010) Fraud detection in reputation systems in e-markets using logistic regression and stepwise optimization. ACM SIGAPP Appl Comput Rev 11(1):14–26. https://doi.org/10.1145/1869687.1869689
Menezes FM, Monteiro PK (2005) An introduction to auction theory. Oxford University Press, Oxford. https://doi.org/10.1093/019927598X.001.0001
Metzler D, Croft WB (2005) A Markov random field model for term dependencies. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pp 472–479. ACM, New York. https://doi.org/10.1145/1076034.1076115
Murphy KP, Weiss Y, Jordan MI (1999) Loopy belief propagation for approximate inference: an empirical study. In: Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence (UAI). Morgan Kaufmann Publishers, San Francisco, pp 467–475
Pandit S, Chau DH, Wang S, Faloutsos C (2007) NetProbe: a fast and scalable system for fraud detection in online auction networks. In: Proceedings of the 16th International Conference on World Wide Web (WWW). ACM, New York, pp 201–210. https://doi.org/10.1145/1242572.1242600
Parhizkar E, Abadi M (2015) BeeOWA: a novel approach based on ABC algorithm and induced OWA operators for constructing one-class classifier ensembles. Neurocomputing 166:367–381. https://doi.org/10.1016/j.neucom.2015.03.051
Reichardt J, Bornholdt S (2007) Clustering of sparse data via network communities—a prototype study of a large online market. J Stat Mech Theory Exp 2007(06):1–19. https://doi.org/10.1088/1742-5468/2007/06/P06016
Schölkopf B, Platt JC, Shawe-Taylor JC, Smola AJ, Williamson RC (2001) Estimating the support of a high-dimensional distribution. Neural Comput 13(7):1443–1471. https://doi.org/10.1162/089976601750264965
Tsang S, Koh YS, Dobbie G, Alam S (2014) Detecting online auction shilling frauds using supervised learning. Expert Syst Appl 41(6):3027–3040. https://doi.org/10.1016/j.eswa.2013.10.033
Tsang S, Koh YS, Dobbie G, Alam S (2014) SPAN: finding collaborative frauds in online auctions. Knowl Based Syst 71:389–408. https://doi.org/10.1016/j.knosys.2014.08.016
Yedidia JS, Freeman WT, Weiss Y (2003) Understanding belief propagation and its generalizations. In: Lakemeyer G, Nebel B (eds) Exploring artificial intelligence in the new millennium. Morgan Kaufmann Publishers, San Francisco, pp 239–269
You W, Liu L, Xia M, Lv C (2011) Reputation inflation detection in a Chinese C2C market. Electron Commer Res Appl 10(5):510–519. https://doi.org/10.1016/j.elerap.2011.06.001
Yu CH (2016) A fuzzy genetic approach for optimization of online auction fraud detection. In: Hung JC, Yen NY, Li KC (eds) Frontier computing, LNEE. Springer, Singapore, pp 965–974. https://doi.org/10.1007/978-981-10-0539-8_94
Yu CH, Lin SJ (2013) Fuzzy rule optimization for online auction frauds detection based on genetic algorithm. Electron Commer Res 13(2):169–182. https://doi.org/10.1007/s10660-013-9113-4
Zhang X, Lishan C, Wang Y (2014) CommTrust: computing multi-dimensional trust by mining e-commerce feedback comments. IEEE Trans Knowl Data Eng 26(7):1631–1643. https://doi.org/10.1109/TKDE.2013.177
Conflict of interest
The authors declare that they have no conflict of interest.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Dadfarnia, M., Adibnia, F., Abadi, M. et al. Incremental collusive fraud detection in large-scale online auction networks. J Supercomput (2020). https://doi.org/10.1007/s11227-020-03170-9
- Collusive auction fraud
- Incremental reputation updating
- Markov random field
- Online auction network
- Semi-supervised anomaly detection