Understanding and Defending Against Malicious Crowdsourcing

Transcription

Understanding and Defending Against Malicious Crowdsourcing
Understanding and Defending Against Malicious Crowdsourcing
SANDLab
http://sandlab.cs.ucsb.edu
[email protected]
Ben Y. Zhao (PI), Haitao Zheng (CoPI), Gang Wang (PhD student), University of California at Santa Barbara
1. Malicious Crowdsourcing
New Threat: Malicious Crowdsourcing = Crowdturfing
+ Hire a large group of real Internet users for malicious attacks
+ Fake reviews, rumors, targerted spam
+ Most existing defenses failed against real users (e.g., CAPTCHA)
2. Understanding Crowdturfing
Key Players
Target Networks
Crowd-workers
+ Customers: pay to run a campaign
+ Workers: real users, spam for $
+ Target Networks: social networks, revew sites
Scale and Revenue
+ How does crowdturfing work?
[1]
+ What’s the scale, economics and impact of crowturfing campaigns?
[2]
+ How to defend against crowdturfing?
[1]
Crowdturfing around the World
3. Defense: Machine Learning Classifiers
Machine Learning (ML) vs. Crowdturfing
+ Simple method does not work on real users (e.g., CAPTCHA, rate limit)
+ Machine learning: more sophiscaed modeling on user behaviors
+ Perfect context to study adversarial machine learning
- Human workers are adaptive to evade classifiers
- Crowdturf admins can temper with training data by chaning worker behaviors
How Effective is ML-based Detecor?
+ Groundtruth: 28K workers in crowdturfing campaigns on Weibo (Chinenes Twitter)
+ Baseline users: 371K Weibo user accounts
+ 30 behavioral features
+ Classiiers: Random Forest, Decision Tree, SVM, Naive Bayes, Bayesian Network
False Positive Rate
False Negative Rate
+ Random Forest is the most accurate (95% accuracy)
+ 99% accuracy on professional workers (>100 tasks)
+ How robust are those classifiers?
ZBJ, SDH
1000
100
10000
$
ZBJ
1000
SDH
10
Campaigns
1
Jan. 2008
$
100
10
Campaigns
Jan. 2009
Jan. 2010
1
Jan. 2011
Fiverr, Freelancer, MinuteWorkers, Myeasytasks, Microworkers, Shorttasks
Adversarial Machine Learning
+ Evasion attack: individual workers change behaviors
to evade the detection
- Impact: single feature-change saves 95% of workers
+ Poisoning attack: site admins tamper with training data
to mislead classifier training
Dollars per Month
Research Questions
1000000
100000
Paisalive
Detection
Model Training
Training
e.g. SVM
Classifier
Training Data
Poisning Attack
Evasion Attack
Example: Poisoning Attack
+ Inject mislabeled samples to training data wrong classifier
e.g., inject benign accounts as “workers” in training data
+ Uniformly change workers behavior by enforcing task policies
hard to train an accurate classifier
Summary
More accurate classifiers
can be more vulnerable
20
False Positive Rate
+ Web services that recruit Internet users as workers (spam for $)
+ Connect workers to customers who want to run malicious campaigns
+ Measurements of two largest crowdturfing sites (in China)
- ZBJ (zhubajie.com), five years
- SDH (sandaha.com), two yeras
+ 18.5M tasks, 79K campaigns, 180K workers
+ Millions dollars of revenue per month
Site Growth Over Time
10000
Campaigns per Month
Crowdturfing Sites
60%
50%
40%
30%
20%
10%
0%
Customer
Crowdturfing Site
15
Tree
10
RF
SVMp
5
0
SVMr
0
0.2
0.4
0.6
0.8
1
Ratio of Injected Sample to Turfing
+ Machine learning classifiers are effective against current crowd-workers
+ Classifiers are highly vulnerable to adversarial attacks. Future works will focus on improving the
robustiness of ML-classifiers
[1] G. WANG, T. WANG, H. ZHENG, B. ZHAO. Man vs. machine: practical adversarial detection of malicious crowdsourcing workers.
In Proc. of Usenix Security (2014)
RF
Tree SVMr SVMp NB
BN
[2] G. WANG, C. WILSON, X. ZHAO, Y. ZHU, M. MOHANLAL, H. ZHENG, B. ZHAO. Serf and turf: crowdturfing for fun and profit.
In Proc. of WWW (2012)

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