Adaptive Detection of Shill Bidding and Multi-Objective Winner Determination

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Two significant research challenges to be addressed in electronic (e) auctions are: monitoring auctions for fraud, and determining the winners for advanced auctions. Due to the anonymity of users and large amounts of money involved, e-auctions are very attractive to fraudsters. Detecting fraud in e-auctions is challenging because they constitute a voluminous and dynamic market. Shill Bidding (SB), which occurs when someone places a bid to artificially increase the price of an item, is the hardest auction fraud to detect due to its similarity to usual bidding behavior. There are limited studies on applying machine learning to detect SB, and most of them have been conducted offline. Adaptive learning is necessary to improve a classifier's performance over time, which is crucial for fraud detection problems. Our goal is to devise a solution that addresses the current challenges of SB detection. We will develop a high-quality labelled, sampled SB training dataset, which is currently lacking and unavailable; develop an online, adaptive classifier that will evolve continuously with new bidding trends; and develop a fraud verification method as training data are used without any ground truth. We are motivated by solving these problems efficiently based on state-of-the-art techniques for data clustering and labeling, imbalanced data sampling, incremental and decremental classification, and automated fraud verification. The learned SB model classifies on demand new data crawled from eBay where thousands of e-auctions are held daily. It is to be launched at the end of the bidding period but prior to determining the winners to avert financial loss. Our SB classifier will be constantly adjusted with confidently labeled data, and this will improve its performance and the ground truth of training data. Combinatorial Reverse Auctions (CRAs) represent an economical procurement method due to their allocative efficiency. CRAs allow sellers to bid on a bundle of goods or services required by a single buyer. Our aim is to develop a robust Winner Determination (WD) method for advanced CRAs, currently subject to trading constraints and conflicting objectives. Inherent to our problem is a mixture of maximization and minimization of objectives. We will formalize the WD as a Multi-Objective Optimization (MOO) problem for which we search a set of trade-off solutions; each one optimizes the conflicting objectives simultaneously. However, performing this trade-off analysis is computationally intensive. In an auction setting, the execution time is a critical requirement. To address this challenge, we will define an evolutionary MOO-based WD method. As a real case study, we will tailor the new WD method to the electricity market to optimally procure power from different energy sources, and assess its performance. Moreover, we will conduct a performance analysis of the WD method based on large-scale instances of CRAs.