Authors:
Jarrod Trevathan
;
Alan McCabe
and
Wayne Read
Affiliation:
School of Maths, Physics and Information Technology, James Cook University, Australia
Keyword(s):
Online auction fraud, extrema avoidence, prediction, software bidding agent, artificial intelligence.
Related
Ontology
Subjects/Areas/Topics:
Agent-Based Information Systems
;
B2B, B2C and C2C
;
Communication and Software Infrastructure
;
Communication and Software Technologies and Architectures
;
Distributed Intelligent Agents
;
e-Business
;
Enterprise Information Systems
;
Global Communication Information Systems and Services
;
Mobile and Pervasive Computing
;
Security and Privacy
;
Society, e-Business and e-Government
;
Software Agents and Internet Computing
;
Telecommunications
;
Web Information Systems and Technologies
Abstract:
This paper presents a software bidding agent that inserts fake bids on the seller’s behalf to inflate an auction’s price. This behaviour is referred to as shill bidding. Shill bidding is strictly prohibited by online auctioneers, as it defrauds unsuspecting buyers by forcing them to pay more for the item. The malicious bidding agent was constructed to aid in developing shill detection techniques. We have previously documented a simple shill bidding agent that incrementally increases the auction price until it reaches the desired profit target, or it becomes too risky to continue bidding. This paper presents an adaptive shill bidding agent which when used over a series of auctions with substitutable items, can revise its strategy based on bidding behaviour in past auctions. The adaptive agent applies a novel prediction technique referred to as the Extremum Consistency (EC) algorithm, to determine the optimal price to aspire for. The EC algorithm has successfully been used in handwritt
en signature verification for determining the maximum and minimum values in an input stream. The agent’s ability to inflate the price has been tested in a simulated marketplace and experimental results are presented.
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