Fighting Fire With Fire : Crowdsourcing Security Threats and Solutions on the Social Web
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Transcript of Fighting Fire With Fire : Crowdsourcing Security Threats and Solutions on the Social Web
Fighting Fire With Fire:Crowdsourcing Security Threats and Solutions on the Social Web
Gang Wang, Christo Wilson, Manish Mohanlal, Ben Y. ZhaoComputer Science Department, UC Santa Barbara.
A Little Bit About Me 3nd Year PhD @ UCSB Intern at MSR Redmond 2011 Intern at LinkedIn (Security
Team) 2012
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Research Interests: Security and Privacy Online Social Networks Crowdsourcing
Data Driven Analysis and Modling
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Recap: Threats on the Social Web Social spam is a serious problem
10% of wall posts with URLs on Facebook are spam
70% phishing Sybils underlie many attacks on Online Social
Networks Spam, spear phishing, malware distribution Sybils blend completely into the social graph
Existing countermeasures are ineffective Blacklists only catch 28% of spam Sybil detectors from the literature do not work
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Sybil Accounts on Facebook In-house estimates
Early 2012: 54 million August 2012: 83 million 8.7% of the user base
Fake likes VirtualBagel: useless site, 3,000 likes
in 1 week 75% from Cairo, age 13-17• Sybils attacks in large scale
• Advertisers are fleeing Facebook
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Sybil Accounts on Twitter
92% of Newt Gingritch’s followers are Sybils Russian political protests on Twitter
25,000 Sybils sent 440,000 tweets 1 million Sybils controlled overall
Follo
wers
4,000 new followers/d
ay100,000 new followers in 1
day• Twitter is vital infrastructure• Sybils usurping Twitter for political ends
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Talk Outline1. Malicious crowdsourcing sites – crowdturfing
[WWW’12] Spam and Sybils generated by real people Huge threat in China Growing threat in the US
2. Crowdsourced Sybil detection [NDSS’13] If attackers can do it, why not defenders? Can humans detect Sybils? Is this cost effective? Design a crowdsourced Sybil detection system
User Study
7 Outline Intro Crowdturfing
Crowdsourcing Overview What is Crowdturfing How bad is it? Crowdturfing in the US
Crowdsourced Sybil Detection Conclusion
8 We tend to think of spam as “low quality” What about high quality spam and Sybils? Open questions
What is the scope of this problem? Generated manually or mechanically? What are the economics?
High Quality Sybils and Spam
Gang WangMaxGentleman is the bestest male enhancement system avalable. http://cid-ce6ec5.space.live.com/
FAKEStock Photographs
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Black Market Crowdsourcing Amazon’s Mechanical Turk
Admins remove spammy jobs Black market crowdsourcing websites
Spam and fake accounts, generated by real people Major force in China, expanding in the US and IndiaCrowdturfing = Crowdsourcing + Astroturfing
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Crowdturfing Workflow
Customers
Initiate campaigns
May be legitimate businesses
Agents Manage
campaign and workers
Verify completed tasks
Workers Complete
tasks for money
Control Sybils on other websites
Campaign
Tasks
Reports
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Crowdturfing in China
Site
ActiveSince
TotalCampaigns
Workers
Reports
$ forWorkers
$ forSite
Zhubajie
Nov. 2006 76K 169K 6.3M $2.4M $595K
1
10
10
100
1000
10000
100000
1000000Site Growth Over Time
Cam
paig
ns p
er
Mon
th
Dol
lars
per
Mon
th
Jan. 08 Jan. 09 Jan. 10 Jan. 11
Zhubajie
Sandaha
Campaigns
$
Campaigns
$
13
Spreading Spam on Weibo
100 1000 10000 100000 1000000 100000000
102030405060708090
100
Approximate Audience Size per Campaign
CDF
50% of campaigns reach
>100000 users 8% reach>1 million
users• Campaigns reach huge audiences• How effective are these campaigns?
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Travel agency reported sales statistics 2 sales/month before our campaign 11 sales within 24 hours after our campaign Each trip sells for $1500!
Initiate our own campaigns as a customer 4 benign ad campaigns promoting real e-
commerce sites All clicks route through our measurement
server
How Effective is Crowdturfing?
Campaign About Targ
etCos
tTask
sRepor
tsClicks
Cost Per
ClickVacation Advertise for
a discount vacation through a
travel agent
$15 100
108 28 $0.21
QQ 118 187 $0.09Forums 123 3 $0.90
Web Display Ads CPC =
$0.01
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Crowdturfing in America
Other studies support these findings Freelancer
28% spam jobs Bulk OSN accounts, likes, spam Connections to botnet operators
US Sites % Crowdturfing
Legit
Mechanical Turk 12%Bl
ack Market
MinuteWorkers
70%
MyEasyTasks 83%Microworkers 89%ShortTasks 95%
Poultry Markets $20 for 1000
followers Ponzi scheme
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Takeaways Identified a new threat: Crowdturfing
Growing exponentially in size and revenue in China
$1 million per month on just one site Cost effective: $0.21 per click
Starting to grow in US and other countries Mechanical Turk, Freelancer Twitter Follower Markets
Huge problem for existing security systems Little to no automation to detect Turing tests fail
17 Outline Intro Crowdturfing Crowdsourced Sybil Detection
Open Questions User Study Accuracy Analysis System Design
Conclusion
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Crowdsourcing Sybil Defense Defenders are losing the battle against OSN
Sybils Idea: build a crowdsourced Sybil detector
Leverage human intelligence Scalable
Open Questions How accurate are users? What factors affect detection accuracy? Is crowdsourced Sybil detection cost effective?
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User Study Two groups of users
Experts – CS professors, masters, and PhD students Turkers – crowdworkers from Mechanical Turk and
Zhubajie Three ground-truth datasets of full user profiles
Renren – given to us by Renren Inc. Facebook US and India
Crawled Legitimate profiles – 2-hops from our own profiles Suspicious profiles – stock profile images Banned suspicious profiles = Sybils
Stock Picture
Crowdturfing Site
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Progress
Classifying Profiles
BrowsingProfiles
Screenshot of Profile(Links Cannot be
Clicked)
Real or fake?
Why?
Navigation Buttons
Testers may skip around and revisit
profiles
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Experiment Overview
Dataset
# of Profiles
Test Group
# of Teste
rs
Profile per
TesterSybil Legit.
Renren 100 100Chinese Expert
24 100
Chinese Turker
418 10
Facebook US 32 50 US Expert 40 50
US Turker 299 12Facebook India 50 49 India Expert 20 100
India Turker 342 12
Crawled Data
Data from Renren
Fewer Experts
More Profiles per Experts
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Individual Tester Accuracy
0 10 20 30 40 50 60 70 80 90 1000
20
40
60
80
100 Chinese TurkerUS TurkerUS Expert
Accuracy per Tester (%)
CDF
(%)
Not so
good :(
• Experts prove that humans can be accurate• Turkers need extra help…
Awesome!80% of experts
have >90% accuracy!
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Accuracy of the Crowd Treat each classification by each tester
as a vote Majority makes final decision
Dataset Test Group False Positives
False Negatives
Renren Chinese Expert 0% 3%Chinese Turker 0% 63%
Facebook US
US Expert 0% 10%US Turker 2% 19%
Facebook India
India Expert 0% 16%India Turker 0% 50%
Almost Zero False Positives
Experts Perform
OkayTurkers Miss
Lots of Sybils
• False positive rates are excellent• Turkers need extra help against false negatives• What can be done to improve accuracy?
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Eliminating Inaccurate Turkers
0 10 20 30 40 50 60 700
20
40
60
80
100ChinaIndiaUS
Turker Accuracy Threshold (%)
Fals
e N
egat
ive
Rate
(%) Dramatic
Improvement
Most workers are >40% accurate From 60% to
10% False Negatives• Only a subset of workers are removed (<50%)
• Getting rid of inaccurate turkers is a no-brainer
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How Many Classifications Do You Need?
2 4 6 8 10 12 14 16 18 20 22 240
20
40
60
80
100
Classifications per Profile
Erro
r Ra
te (%
)
ChinaIndia
US
False Negatives
False Positives
• Only need a 4-5 classifications to converge• Few classifications = less cost
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How to turn our results into a system?1. Scalability
OSNs with millions of users2. Performance
Improve turker accuracy Reduce costs
3. Preserve user privacy when giving data to turkers
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Social NetworkHeuristics
User ReportsSuspicious Profiles
All Turkers
OSN employee
TurkerSelection Accurate Turkers
Very Accurate Turkers
Sybils
System Architecture
Filtering Layer
Crowdsourcing Layer
Filter Out Inaccurate
TurkersMaximize Usefulness
of High Accuracy Turkers
Rejected!
• Leverage Existing Techniques
• Help the System Scale
?
• Continuous Quality Control
• Locate Malicious Workers
Trace Driven Simulations Simulate 2000 profiles Error rates drawn from survey
data Vary 4 parameters
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Accurate Turkers
Very Accurate Turkers
Classifications
Classifications
Threshold
Controversial Range
Results• Average 6 classifications per profile• <1% false positives• <1% false negatives
2
5
90%
20-50%
Results++• Average 8 classifications per profile• <0.1% false positives• <0.1% false negatives
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Estimating Cost Estimated cost in a real-world social networks: Tuenti
12,000 profiles to verify daily 14 full-time employees Annual salary 30,000 EUR (~$20 per hour) $2240 per day
Crowdsourced Sybil Detection 20sec/profile, 8 hour day 50 turkers Facebook wage ($1 per hour) $400 per day
Cost with malicious turkers Estimate that 25% of turkers are malicious 63 turkers $1 per hour $504 per day
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Takeaways Humans can differentiate between real and
fake profiles Crowdsourced Sybil detection is feasible Designed a crowdsourced Sybil detection
system False positives and negatives <1% Resistant to infiltration by malicious workers Sensitive to user privacy Low cost
Augments existing security systems
31 Outline Intro Crowdturfing Crowdsourced Sybil Detection Conclusion
Summary of My Work Future Work
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Key Contributions1. Identified novel threat: crowdturfing
End-to-end spam measurements from customers to the web
Insider knowledge of social spam2. Novel defense: crowdsourced Sybil
detection User study proves feasibility of this approach Build an accurate, scalable system Possible deployment in real OSNs – LinkedIn
and RenRen
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Ongoing Works1. Twitter follower markets
Locate customers who purchase bulk of Twitter followers
Study the un-follow dynamics of customers Develop systems to detect customers in the wild
2. Sybil detection using server-side click streams Build click models based on clickstream logs Extract click patterns of Sybil and normal users Develop systems to detect Sybil
34 Questions?
Thank you!
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Potential Project Ideas Malware distribution in cellular networks
Identify malware related cellular network traffic Coordinated malware distribution campaigns Feature based detection
Advertising traffic analysis on mobile Apps Characterize ads traffic How effective for app-displayed ads to get click-
through? Are there malware delivered through ads?
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Preserving User Privacy Showing profiles to crowdworkers raises
privacy issues Solution: reveal profile information in
context!
Crowdsourced
Evaluation!
Crowdsourced
Evaluation
Public Profile
Information
Friend-Only
Profile Informatio
nFriends
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Clickstream Sybil Detection
Sybil Clickstream
Friend
Invite
Share
Browse
Profiles
Initial Final
96%
9%
68%
15% 2%
27%64%
20% 55%31%
Photo
Initial Final22% 3%
Share
MessageFrien
d Invite
Browse
Profiles
9% 4%
5%5%14%
9%
21%56%
56%
29%
86%87%
10%43%
14%93%
Normal Clickstream
Clickstream detection of Sybils1. Absolute number of
clicks2. Time between clicks3. Page traversal order
Challenges Real-time Massive scalability Low-overhead
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Are Workers Real People?
0 5 10 15 200123456789
ZhubajieSandaha
Hours in the Day
% o
f Rep
orts
from
W
orke
rs
Late Night/Early Morning Work Day/Evening
Lunch Dinn
erZBJSDH
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Crowdsourced Sybil Detection How to detect crowdturfed Sybils?
Blur the line between real and fake Difficult to detect algorithmically
Anecdotal evidence that people can spot Sybils 75% of friend requests from Sybils are rejected Can people distinguish in real/fake general?
User studies: experts, turkers, undergrads What features give Sybils away? Are certain Sybils tougher than others?
Integration of human and machine intelligence
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Survey Fatigue US Experts US Turkers
0 3 6 90
20
40
60
80
100
0
20
40
60
80
100
Profile OrderTi
me
per
Profi
le (s
)
Accu
racy
(%)
No fatigue
0 8 16 24 32 40 480
20
40
60
80
100
0
20
40
60
80
100
AccuracyProfile Order
Tim
e pe
r Pr
ofile
(s)
Accu
racy
(%)
Fatigue matters
All testers speed up over time
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Sybil Profile Difficulty
0 5 10 15 20 25 30 350
102030405060708090
100
Turker
Sybil Profiles Ordered By Turker Accuracy
Aver
age
Accu
racy
per
Sy
bil (
%)
Experts perform well on most difficult Sybils
Really difficult profiles
• Some Sybils are more stealthy• Experts catch more tough Sybils than turkers