We looked at how to augment crowdsourcing techniques to improve coverage, accuracy, and timeliness in identifying phishing attacks. We used relatively simple clustering algorithms to group phish together, as well as weighting votes based on previous correct answers.

1. ©2009CarnegieMellonUniversity:1
Smartening the Crowds:
Computational Techniques for
Improving Human Verification
to Fight Phishing Scams
Gang Liu
Wenyin Liu
Department of Computer Science
City University of Hong Kong
Guang Xiang
Bryan A. Pendleton
Jason I. Hong
Carnegie Mellon University

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Detecting Phishing Websites
• Method 1: Use heuristics
– Unusual patterns in URL, HTML, topology
– Approach is favored by researchers
– High true positives, some false positives
• Method 2: Manually verify
– Approach used by industry blacklists today
(Microsoft, Google, PhishTank)
– Very few false positives, low risk of liability
– Slow, easy to overwhelm

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Wisdom of Crowds Approach
• Mechanics of PhishTank
– Submissions require at least 4 votes
and 70% agreement
– Some votes weighted more
• Total stats (Oct2006 – Feb2011)
– 1.1M URL submissions from volunteers
– 4.3M votes
– resulting in about 646k identified phish
• Why so many votes for only 646k phish?

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PhishTank Statistics
Jan 2011
Submissions 16019
Total Votes 69648
Valid Phish 12789
Invalid Phish 549
Median Time 2hrs 23min
• 69648 votes → max of 17412 labels
– But only 12789 phish and 549 legitimate identified
– 2681 URLs not identified at all
• Median delay of 2+ hours still has room
for improvement

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Why Care?
• Can improve performance of
human-verified blacklists
– Dramatically reduce time to blacklist
– Improve breadth of coverage
– Offer same or better level of accuracy
• More broadly, new way of improving
performance of crowd for a task

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Ways of Smartening the Crowd
• Change the order URLs are shown
– Ex. most recent vs closest to completion
• Change how submissions are shown
– Ex. show one at a time or in groups
• Adjust threshold for labels
– PhishTank is 4 votes and 70%
– Ex. vote weights, algorithm also votes
• Motivating people / allocating work
– Filtering by brand, competitions,
teams of voters, leaderboards

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Ways of Smartening the Crowd
• Change the order URLs are shown
– Ex. most recent vs closest to completion
• Change how submissions are shown
– Ex. show one at a time or in groups
• Adjust threshold for labels
– PhishTank is 4 votes and 70%
– Ex. vote weights, algorithm also votes
• Motivating people / allocating work
– Filtering by brand, competitions,
teams of voters, leaderboards

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Ways of Smartening the Crowd
• Change the order URLs are shown
– Ex. most recent vs closest to completion
• Change how submissions are shown
– Ex. show one at a time or in groups
• Adjust threshold for labels
– PhishTank is 4 votes and 70%
– Ex. vote weights, algorithm also votes
• Motivating people / allocating work
– Filtering by brand, competitions,
teams of voters, leaderboards

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Ways of Smartening the Crowd
• Change the order URLs are shown
– Ex. most recent vs closest to completion
• Change how submissions are shown
– Ex. show one at a time or in groups
• Adjust threshold for labels
– PhishTank is 4 votes and 70%
– Ex. vote weights, algorithm also votes
• Motivating people / allocating work
– Filtering by brand, competitions,
teams of voters, leaderboards

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Ways of Smartening the Crowd
• Change the order URLs are shown
– Ex. most recent vs closest to completion
• Change how submissions are shown
– Ex. show one at a time or in groups
• Adjust threshold for labels
– PhishTank is 4 votes and 70%
– Ex. vote weights, algorithm also votes
• Motivating people / allocating work
– Filtering by brand, competitions,
teams of voters, leaderboards

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Overview of Our Work
• Crawled unverified submissions from
PhishTank over 2 week period
• Replayed URLs on MTurk over 2 weeks
– Required participants to play
2 rounds of Anti-Phishing Phil
– Clustered phish by html similarity
– Two cases: phish one at a time, or in a
cluster (not strictly separate conditions)
– Evaluated effectiveness of vote weight
algorithm after the fact

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Anti-Phishing Phil
• We had MTurkers play two rounds of
Phil [Sheng 2007] to qualify (µ = 5.2min)
• Goal was to reduce lazy MTurkers and
ensure base level of knowledge

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Clustering Phish
• Observations
– Most phish are generated by toolkits and
thus are similar in content and appearance
– Can potentially reduce labor by labeling
suspicious sites in bulk
– Labeling single sites as phish can be hard
if unfamiliar, easier if multiple examples

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Clustering Phish
• Motivations
– Most phish are generated by toolkits and
thus similar
– Labeling single sites as phish can be hard,
easier if multiple examples
– Reduce labor by labeling suspicious sites
in bulk

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Clustering Phish
• Motivations
– Most phish are generated by toolkits and
thus similar
– Labeling single sites as phish can be hard,
easier if multiple examples
– Reduce labor by labeling suspicious sites
in bulk

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Most Phish Can be Clustered
• With all data over two weeks, 3180 of
3973 web pages can be grouped (80%)
– Used shingling and DBSCAN (see paper)
– 392 clusters, size from 2 to 153 URLs

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MTurk Tasks
• Two kinds of tasks, control and cluster
– Listed these two as separate HITs
– MTurkers paid $0.01 per label
– Cannot do between-conditions on MTurk
– MTurker saw a given URL at most once
• Four votes minimum, 70% threshold
– Stopped at 4 votes, cannot dynamically
request more votes on MTurk
– 153 (3.9%) in control and 127 (3.2%) in
cluster not labeled

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MTurk Tasks
• URLs were replayed in order
– Ex. If crawled at 2:51am from PhishTank
on day 1, then we would replay at 2:51am
on day 1 of experiment
– Listed new HITs each day rather than a
HIT lasting two weeks (to avoid delays
and last minute rush)

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Summary of Experiment
• 3973 suspicious URLs
– Ground truth from Google, MSIE, and
PhishTank, checked every 10 min
– 3877 were phish, 96 not
• 239 MTurkers participated
– 174 did HITs for both control and cluster
– 26 in Control only, 39 in Cluster only
• Total of 33,781 votes placed
– 16,308 in control
– 11,463 in cluster (17473 equivalent)
• Cost (participants + Amazon): $476.67 USD

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Results of Aquarium
• All votes are the individual votes
• Labeled URLs are after aggregation

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Comparing Coverage and Time

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Voteweight
• Use time and accuracy to weight votes
– Those who vote early and accurately
are weighted more
– Older votes discounted
– Incorporates a penalty for wrong votes
• Done after data was collected
– Harder to do in real-time since we don’t
know true label until later
• See paper for parameter tuning
– Of threshold and penalty function

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Voteweight Results
• Control condition best scenario
– Before-after
– 94.8% accuracy, avg 11.8 hrs, median 3.8
– 95.6% accuracy, avg 11.0 hrs, median 2.3
• Cluster condition best scenario
– Before-after
– 95.4% accuracy, avg 1.8 hrs, median 0.7
– 97.2% accuracy, avg 0.8 hrs, median 0.5
• Overall: small gains, potentially more
fragile and more complex though

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Limitations of Our Study
• Two limitations of MTurk
– No separation between control and cluster
– ~3% tie votes unresolved (more votes)
• Possible learning effects?
– Hard to tease out with our data
– Aquarium doesn’t offer feedback
– Everyone played Phil
– No condition prioritized over other
• Optimistic case, no active subversion

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Conclusion
• Investigated two techniques for
smartening the crowd for anti-phishing
– Clustering and voteweight
• Clustering offers significant
advantages wrt time and coverage
• Voteweight offers smaller
improvements in effectiveness

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Acknowledgments
• Research supported by CyLab under
ARO DAAD19-02-1-0389 and
W911NF-09-1-0273
• Research Grants Council of the Hong
Kong Special Administrative Region,
China [Project No. CityU 117907]

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Individual Accuracy

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4. Framework

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4.1 whitelists
 include 3208 domains
 From Google safe browsing (2784)
 http://sb.google.com/safebrowsing/update?version
=goog-white-domain:1:1
 From millersmiles (424)
 http://www.millersmiles.co.uk/scams.php
 Reduce false positive
 Save human effort

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4.2 Clustering
 Content similarity measurement (Shingling method)
 S(q),S(d) denote the set of unique n-grams in page q and d
 The threshold is 0.65
 The average time cost : 0.063 microseconds (SD=0.05)
 calculating similarity of two web pages on a laptop with 2GHz
dual core CPU with 1 GB of RAM
 DBSCAN
 Eps=0.65 and MinPts=2.
 The time cost of clustering over all 3973 pages collected
was about 1 second.
( )
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( ) ( )dSqS
dSqS
dqr


=,

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4.2 Clustering
 Incremental update of the data
 If there is no similar web page, we create a new cluster for
the new submission.
 If the similarity is above the given threshold and all similar
web pages are in the same cluster, we assign the new
submission to this cluster (unless the cluster is at its
maximum size).
 If there are many similar web papes in different clusters,
we choose the largest cluster that is not at its maximum
size.
 After a new submission is grouped in a cluster
 It has zero votes and does not inherit the votes of any other
submissions in the same cluster.

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4.3 Voteweight
The core idea behind voteweight is that participants
who are more helpful in terms of time and accuracy are
weighted more than other participants.
 It measures how powerful of users’ votes impact on the final
status of the suspicious URLs.
 Its value comes from the accuracy of users’ historical data
 a correct vote should be rewarded and a wrong one should
be penalized
 recent behavior should be weighted more than past behavior

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4.3 Voteweight
 In our model, we use y {t,+∞} y {-t,-∞}∈ ∪ ∈ to label the
status of a URL,
 where y is the sum of voteweight of a given URL,
 t is the threshold of voteweight,
 y≥ t means a URL has been voted as a phishing
URL
 y≤-t means voted as legitimate.

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4.3 Voteweight
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7. Investigating Voteweight
Tuning Parameters in Control Condition
 Voteweight achieves its best accuracy 95.6% and time cost of 11
hours with t=0.08 and α=2.5 in the control condition
 Average time cost drops to 11 hours (11.8 hours without
voteweight)
 Median time cost drops to 2.3 hours (3.8 hours without woteweight)

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7. Investigating Voteweight
Tuning Parameters in Cluster Condition
 Voteweight achieves its best accuracy of 97.2% and time cost of
0.8 hours with t=0.06 and α= 1 in the control condition
 Average time cost drops to 0.8 hours (1.8 hours without
voteweight)
 Median time cost drops to 0.5 hours (0.7 hours without
woteweight)