Abstract
Designing effective shill bidding detection and prevention mechanisms in a cloud auction house is one of the main challenges of the cloud market. In this paper, one mechanism for shill bidding detection and one for its prevention are focused on. Two objectives are considered in designing these mechanisms: (1) increase the accuracy of shill bidding detection mechanism, and (2) decrease fraud activities of shill bidders while increasing the profit of honest bidders. The accuracy of a shill bidding detection mechanism can be improved by combining results of run-time monitoring of bidding behavior in running an auction and results of bidding behavior obtained from past auctions. Thus, a new hybrid shill detection mechanism is proposed. Also, our idea in designing of shill bidding prevention mechanism is shaped based on the fact that shill bidders continue their fraudulent behaviors only when they are in trading spaces that are created by sellers who have colluded with them. To do this, a genetic algorithm (GA)-based approach is developed to create appropriate trading spaces for honest bidders aiming at minimizing suspicious activities as well as maximizing trading opportunities. Consequently, honest bidders are hosted by more profitable and healthier trading spaces in which the probability of meeting shill bidders and fraud sellers is decreased dramatically. The proposed ideas are supported by a multi-agent auction system. Simulation results prove the success of the designed auction system.
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The genetic algorithm is used to create appropriate sub-trading spaces in an auction room before the start of each monitoring stage. Since the auction room is divided into three monitoring stages, the genetic algorithm is applied three times.
Termination of the auction room is caused by: (1) selling all auctioned resources, or (2) expiration of auction time.
Abbreviations
- GA:
-
Genetic algorithm
- VM:
-
Virtual machine
- MAS:
-
Multi-agent system
- CDARA:
-
Combinatorial double auction resource allocation
- CMM:
-
Cloud market maker
- AHM:
-
Auction house manager
- ARM:
-
Auction room manager
- CF_DB:
-
Customers’ feedback database
- HC_DB:
-
History of customers’ trading activities database;
- ROTA_DB:
-
Results of online trading activities database;
- FA:
-
Fraudulent activity
- TO:
-
Trading opportunity
- CIP:
-
Customer initial price
- QoS:
-
Quality of service
- WR:
-
Winning ratio
- ETBFB:
-
Elapsed time before first bid
- RTALB:
-
Remaining time after last bid
- BF:
-
Bid frequency
- ABI::
-
Average bid increment
- EFAHW:
-
Effectiveness of fraud activities on honest winners
- UA:
-
Utility-driven approach
- LSS:
-
Live shill score
- FDUA:
-
Fraud detection and utility-driven approach
- PSHP:
-
Percentage of shill participants
- CPR:
-
Customer to provider ratio
- NMP:
-
Number of market participants
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Adabi, S., Farhadinasab, H. & Jahanbani, P.R. A genetic algorithm-based approach to create a safe and profitable marketplace for cloud customers. J Ambient Intell Human Comput 13, 2381–2413 (2022). https://doi.org/10.1007/s12652-021-03682-z
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DOI: https://doi.org/10.1007/s12652-021-03682-z