CAFD: Detecting Collusive Frauds in Online Auction Networks by Combining One-Class Classification and Collective Classification

Habibollahi, Nazanin; Abadi, Mahdi; Dadfarnia, Mahila

doi:10.1109/iscisc.2017.8488364

Cited by 1 publication

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“…Over the years, researchers from different disciplines have carried out a series of studies to detect fraudulent users in OANs [23,36]. Nevertheless, the detection of auction frauds is still a challenging task, especially when multiple sellers and bidders participate or collude in fraud [8,12,28,33]. While collusive auction frauds are not as common as those perpetrated by individual users, they are more harmful because they are more difficult to detect and often result in higher amounts of financial losses.…”

Section: Introductionmentioning

confidence: 99%

Incremental collusive fraud detection in large-scale online auction networks

et al. 2020

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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.

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