Cited By
View all- Liang FHatcher WLiao WGao WYu W(2019)Machine Learning for Security and the Internet of Things: The Good, the Bad, and the UglyIEEE Access10.1109/ACCESS.2019.29489127(158126-158147)Online publication date: 2019
Penny Auctions are Predictable: Predicting and Profiling User Behavior on DealDash
Pages 123 - 127
Abstract
We study user behavior and the predictability of penny auctions, auction sites often criticized for misrepresenting themselves as low-price auction marketplaces. Using a 166-day trace of 134,568 auctions involving 174 million bids on DealDash, the largest penny auction site in service, we show that a) both the timing and source of bids are highly predictable, and b) users are easily classified into clear behavioral groups by their bidding behavior, and such behaviors correlate highly with the eventual profitability of their bidding strategies. This suggests that penny auction sites are vulnerable to modeling and adversarial attacks.
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Index Terms
- Penny Auctions are Predictable: Predicting and Profiling User Behavior on DealDash
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Published In
July 2018
266 pages
ISBN:9781450354271
DOI:10.1145/3209542
- General Chair:
- Dongwon Lee,
- Program Chairs:
- Nishanth Sastry,
- Ingmar Weber
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Published: 03 July 2018
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HT '18
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HT '18 Paper Acceptance Rate 19 of 69 submissions, 28%;
Overall Acceptance Rate 378 of 1,158 submissions, 33%
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View all- Liang FHatcher WLiao WGao WYu W(2019)Machine Learning for Security and the Internet of Things: The Good, the Bad, and the UglyIEEE Access10.1109/ACCESS.2019.29489127(158126-158147)Online publication date: 2019
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