Authors:
Yi-Tsung Cheng
and
Haiping Xu Computer
Affiliation:
University of Massachusetts Dartmouth, United States
Keyword(s):
Concurrent online auctions, Shilling behaviors, Model checking, Linear temporal logic (LTL).
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Process Management
;
e-Business
;
Enterprise Engineering
;
Enterprise Information Systems
;
Information Engineering Methodologies
;
Information Systems Analysis and Specification
;
Knowledge Management and Information Sharing
;
Knowledge-Based Systems
;
Modeling Concepts and Information Integration Tools
;
Modeling Formalisms, Languages and Notations
;
Modeling of Distributed Systems
;
Symbolic Systems
Abstract:
Shilling behaviors are one of the most serious fraud problems in online auctions, which make winning bidders have to pay more than what they should pay for auctioned items. In concurrent online auctions, where multiple auctions for the same type of items are running simultaneously, shilling behaviors can be even more severe because detecting, predicting and preventing such fraudulent behaviors become very difficult. In this paper, we propose a formal approach to detecting shilling behaviors in concurrent online auctions using model checking techniques. We first develop a model template that represents two concurrent online auctions in Promela. Based on the model template, we derive an auction model that simulates the bidding processes of two concurrent auctions. Then we use the SPIN model checker to formally verify if the auction model satisfies any questionable behavioral properties that are written in LTL (Linear Temporal Logic) formulas. Thus, our approach simplifies the problem o
f searching for shilling behaviors in concurrent online auctions into a model checking problem. Finally, we provide a case study to illustrate how our approach can effectively detect possible shill bidders.
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