Post on 13-Sep-2020
An Auctioning Reputation System Based on Anomaly Detection
Shai Rubin, Mihai ChristodorescuVinod Ganapathy, Jonathon T. Giffin,
Louis Kruger, Hao Wang, Nicholas Kidd
Computer Sciences Dept.University of Wisconsin
Madison
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Online Auctioning• Huge volume: eBay hosted 440,100,000 new
listings in Q2 2005• In this talk: trustworthiness of online auctioning • Why do we buy in an online auction?
A. to find a rare/collectable itemB. to find a bargain; commodity at a “good” price
• eBay financial report (expected 2005):– Clothing & Accessories --- $3.3 billion (2nd)– Consumer Electronics --- $3.2 billion (3rd)– Computers --- $2.9 billion (4th)
Data suggests that most people use eBay to find bargains
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Finding a Bargain is Tricky
• Inherently untrustworthy environment: – Pseudonymous sellers– Pseudonymous buyers – Delivery? Warranty? Quality?
• Reputation system: a tool to establish trust
4
Finding a Bargain is Tricky
• eBay’s reputation system provides little help– Based on feedback: vulnerable to “poisoning” attack
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Finding a Bargain is Tricky
• eBay’s reputation system provides little help– Based on feedback: vulnerable to “poisoning” attack – Does not provide information on price
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Finding a Bargain is Tricky
• eBay’s reputation system provides little help– Based on feedback: vulnerable to “poisoning” attack – Does not provide information on price– Does not differentiate among the majority of sellers
60%
70%
80%
90%
100%
0% 20% 40% 60% 80% 100%
% of sellers (1545 sellers with more than 10 auctions)
90% of sellers:Positive feedback > 97.3%
50% of sellers:Positive feedback > 99.4%
% of positivefeedback
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Goals• Alice—a buyer, Bob—a seller• Develop a trustworthy mechanism that helps Alice:
– Achieve her goal: what are the chances that Alice can find a bargain in Bob’s auctions?
– Warn her from fraudulent activities: are the prices in Bob’s auctions artificially inflated?
– Provide her assurance against poisoning attack: why should Alice trust the mechanism?
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Contributions
• A reputation system that helps buyers avoid sellers who seem to be inflating prices– Formulated the “seem to be inflating prices” as an
anomaly detection problem – Business level anomaly detection: the basic events are
auctions, bidding. – Behavioral system: based on how human behave/act
rather than on people feedback. • Only a first step, some goals still ahead
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Outline
• Motivation: find a bargain and avoid fraud• Contributions: anomaly detection system to
identify price inflating sellers:– The N model– The M model– The P model
• Case studies
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Auctioning 101• Pseudonymous sellers and bidders • Auctions end after a predefined time (e.g., 7 days)• Highest bid wins • Seller sets minimum starting bid• Shilling: a group of bidders that place fake bids to
inflate the final price
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Methodology• Collect data from eBay
– three weeks of data in the category: Laptop Parts & Accessories
– 127,815 auctions, 12,331 sellers, – 604 high-volume sellers: posted more than 14 auctions
controls 60% of the market• Use statistical model to predict seller behavior
– 95% of the sellers are “normal”– 5% are abnormal, or suspicious
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Step 1: Average Number of Bids• What is an indication that prices are high?
– high number of bids • Goal: identify sellers with abnormally high
number of bids
0 2 4 6 8 10 12 14 160
5
10
15
20
25
30
35
% o
f hig
h−vo
lum
e se
llers
Average number of bids per auction
5%95%
5%95%
Average bids per auction
% o
f hig
h-vo
lum
e se
llers
• 95% of high-volume sellers have less than 7 bids per auction
•Model is insensitive to supply: number of auctions posted by a seller
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Step 1: The N Model
0 2 4 6 8 10 12 14 16
200
400
600
800
1000
1200
1400
1600
Average number of bids per auction
Num
ber
of a
uctio
ns fo
r a
selle
r
10260
N normalN abnormalNormal line
Num
ber o
f auc
tions
for s
elle
r
Suspicious:5% of high-volume sellers
Correlation: many auctions implies low number of bids
Suspicious seller: one that posts many auctions and still attracts many bids
Average bids per auction
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Outline
• Motivation: find a bargain and avoid fraud• Contributions: anomaly detection system to
identify price inflating sellers:– The N model: a seller is suspicious if they post many
auctions that attract many bids– The M model– The P model
• Reputation example
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Step 2: Average Minimum Starting Bid• Legitimate explanation for high number of bids:
low minimum starting bid • Goal: identify sellers with abnormally high
number of bids and high minimum bid• Problem: how do you know that the minimum bid
is high?
dwinning_bidminimum_bidwinning_bi −Relative minimum
bid (RMB) =
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Step 2: The M Model
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
2
4
6
8
10
12
14
16
10260
Average RMB for seller
Ave
rage
# o
f bid
s in
sel
ler’s
auc
tions
M normalM abnormalPolynomial fitNormal line
Ave
rage
# o
f bid
s in
sel
ler’s
auc
tions
Average relative minimum bid for seller
Suspicious: 5% of high-volume sellers
Correlation: low minimum starting bid implies many bids
M suspicious seller: starts with high minimum bid and attracts many bids
M+N suspicious seller: posts many auctions, attracts many bids, starts with high minimum bid
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Step 3: Bidders’ Profile of a Seller • Fraudulent explanation for high number of bids:
shilling • Goal: identify group of bidders that repeatedly
bid and lose in a seller’s auctions• Suspicious seller:
– N: sellers with abnormally high number of bids and– M: high starting bid and– P: has a group of bidders that repeatedly bid and lose
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Bidder Presence Curve
0 10 20 30 40 50 60 70 80 90 1000
20
40
60
80
100P
artic
ipat
ed a
uctio
ns (
%)
Bidders (%)
Bidder presence
0 10 20 30 40 50 60 70 80 90 1000
20
40
60
80
100
Bidders (%)
Par
ticip
ate
or w
in (
%)
Bidder presenceBidder wins
Bidders %
5% of bidders in this seller’s auctions participated in 80% of the auctions
Participated auctions %
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Bidder Presence/Win Curves
0 10 20 30 40 50 60 70 80 90 1000
20
40
60
80
100P
artic
ipat
ed a
uctio
ns (
%)
Bidders (%)
Bidder presence
0 10 20 30 40 50 60 70 80 90 1000
20
40
60
80
100
Bidders (%)
Par
ticip
ate
or w
in (
%)
Bidder presenceBidder wins
Bidders %
Participated auctions %
the same 5% won only 10% of the auctions
Bidders %
Participatedor won
auctions %
5% of bidders in this seller’s auctions participated in 80% of the auctions
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0 10 20 30 40 50 60 70 80 90 1000
20
40
60
80
100
Par
ticip
ated
auc
tions
(%
)
Bidders (%)
C Normal lineBidder presence
0 10 20 30 40 50 60 70 80 90 1000
20
40
60
80
100
Bidders (%)
Par
ticip
ate
or w
in (
%)
(d) Cumulative bidder presence and bidder wins curves
C Bidder presenceBidder wins
Bidder Presence/Win Curves(Normal case)
Bidders %
Participatedor won
auctions %
10% of the bidders participated in 20% of the auctions and won 20% of the times
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Outline
• Motivation: find a bargain and avoid fraud• Contributions: anomaly detection system to
identify price inflating sellers:– The N model: a seller is suspicious if they post many
auctions that attract many bids – The M model: a seller is suspicious if they attract
many bids and start with high minimum bid– The P model: a seller is suspicious if they have a
group of bidders that repeatedly participate and lose• Reputation example
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Reputation Example: Seller 10260
0 2 4 6 8 10 12 14 16
200
400
600
800
1000
1200
1400
1600
Average number of bids per auction
Num
ber o
f auctions for a
seller
10260
N normalN abnormalNormal line
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
2
4
6
8
10
12
14
16
10260
Average RMB for seller
Average #
of bid
s in s
eller’s
auctions
M normalM abnormalPolynomial fitNormal line
0 20 40 60 80 1000
10
20
30
40
50
60
70
80
90
100
Bidders (%)
Pa
rtic
ipa
te
o
r w
in (%
)
Bidder presenceBidder wins
N M P
Bidders %Average relative minimum bid for seller
Average bids per auction
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Results Summary
• 54 sellers classified as abnormal with respect to at least one model
• 3 sellers classified as abnormal with respect to all three models
• No confirmed fraud
N M
P
3
2
21
24 13
9
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Summary
• Trust: do we get what we expected?• Reputation system as anomaly detection
– Attempt to identify price inflation– Work at the business level– Consider poisoning attack (see paper)
Thank you. Questions?