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Identifying and Mitigating Cross-Platform
Phone Number Abuse on Social
Channels
linkedin/in/srishti-gupta-627aa738 @Srishti_Gupta14 fb.com/gupta.srishti14
Committee Members
Dr. Fabricio Benevenuto
Dr. Pawan Goyal
Dr. Sameep Mehta
Dr. Ponnurangam Kumaraguru (Advisor)
Srishti Gupta
PhD Thesis Defense
April 25, 2019
IIIT-Delhi
1
Who am I?
◆ Research Scientist at American Express
◆ PhD student since December, 2013 - IIIT-Delhi
▶ Masters (2011 - 2013, IIIT-Delhi)
◆ Collaborations
▶ New York University (Abu Dhabi), Georgia Institute of
Technology (Atlanta), Microsoft IDC (Hyderabad), Pindrop
Security (Atlanta)
◆ Worked in Privacy and Security in Online Social Networks
◆ Research Interests
▶ Applied Machine Learning
▶ Natural Language Processing
▶ Web Security
2
Motivation
3
4
Keys to the Kingdom!
Outgoing Spam
Communication!
5
What difference does it make?
◆ Numbers under spammer control - no spoofing
◆ Incoming services like Truecaller don’t work for outgoing
spammers
◆ Difference w.r.t URLs?
▶ Medium different, more trust
▶ Minimal defense solutions unlike spam filters
▶ The propagating and damaging channel are different
6
“
What can we do?
7
“
Locate spammers and take them down!
8
Challenges
◆ Lack of useful header data
◆ Difficulty in handling audio streams
◆ Lack of ground truth
◆ Country of origin of phone numbers unknown (toll-free
numbers)
◆ Temporary disposable numbers
9
Thesis Statement
Cross-platform phone-based spam campaigns can be
disintegrated across social channels by identifying and
mitigating spam using relational similarity that thrives on
identifiable and discriminative public attributes
10
Contributions Summary
◆ Building automated frameworks to identify and characterise
phone-based spam campaigns on social channels
◆ Evaluating the effectiveness of existing state-of-art tools in
detecting spam phone numbers
◆ Mitigating phone-based spam campaigns by building
SpamDoctor, a supervised detection method to flag phone
numbers abused on OSNs.
11
SpamDoctor: Demo
12
Contributions Summary
◆ Building automated frameworks to identify and characterise
phone-based spam campaigns on social channels
◆ Evaluating the effectiveness of existing state-of-art tools in
detecting spam phone numbers
◆ Mitigating phone-based spam campaigns by building
SpamDoctor, a supervised detection method to flag phone
numbers abused on OSNs.
13
Targeted Attacks on Over-The-Top (OTT)
Messaging Applications
14
Malicious Entity:
Advertisements,
random contact
requests
Service Provider:
Inefficient filtering
mechanisms
OTT User: Spam
activities not yet
seen
System Architecture
15
Gupta, S., Gupta, P., Ahamad, M., and Kumaraguru, P. Exploiting Phone Numbers and Cross-Application Features in Targeted Mobile
Attacks. Accepted at the 6th Workshop on Security and Privacy in Smartphones and Mobile Devices (SPSM), 2016
Information Gathering
◆ Leveraged using Truecaller
◆ Information like name, address, photo URL, OSN handles
(Twitter and Facebook), e-mail
◆ Facebook Graph API
◆ Public feeds, posts, albums (public sources)
16
Scalability
17
Success: Amazon Mechanical Turk
18
Social (69.2) > Spear (54.3) > Non-targeted (34.5)
“Threat landscape of
phone-based spam
campaigns on Online Social
Networks?
19
System Architecture
20
Start
Gupta, S., Kuchhal, D., Gupta, P., Ahamad, M., Gupta, M. and Kumaraguru, P. "Under the Shadow of Sunshine:
Characterizing Spam Campaigns Abusing Phone Numbers Across Online Social Networks. Accepted in the 10th ACM
Conference on Web Science, Amsterdam, 27-30 May 2018
Data Collection
◆ Tweet Collection: using keywords like: “call”, “ring”, “reach”,
“SMS”, “WhatsApp” etc.
◆ Data stored - phone number, posts, author details, URLs,
suspended accounts’ information
◆ Google - Existing Internet Infrastructure
21
Needle in the haystack!
22
P: Phone number; T: Tweet; U: Unigrams
Ground Truth Creation
◆ Suspended accounts
◆ Overlap with FTC dataset
◆ Overlap with existing Truecaller services
◆ Duplicate posts by single and multiple accounts
23
Dataset
◆ ~22M posts
▶ 22,390 campaigns
▶ 1,845,150 distinct phone numbers
▶ 3,365,017 distinct user accounts
◆ ~4.9M posts
▶ Manually verified 202 campaigns
▶ 2,346 distinct phone numbers
▶ 157,494 distinct user accounts
24
Modus Operandi
◆ Advanced fee
◆ Selling Products
◆ Alternating beliefs
(LoveGuru)
◆ Tech Support
25
Where does Phone Spam Originate?
◆ Country code using Google libphonenumber
◆ Automated calling using
◆ Google Speech API
(Audio to text)
26
Where does Phone Spam Originate?
27
“
How do campaigns spread
across Online Social
Networks?
28
Case Study: Tech Support Campaign
◆ 43,552 posts
◆ Used toll-free numbers
◆ Majority phone numbers registered between 2014 and 2016
Feature Twitter Facebook GooglePlus YouTube Flickr
Total Posts 28,984 2,151 7,830 2,850 1,737
Dis. Phone
Numbers
41 33 37 39 20
Distinct
User IDs
748 289 360 433 79
29
Cross-Pollination
◆ Is particular OSN prefered? Specific pattern?
30
Existing Web Intelligence useful?
◆ 68.7% accounts never suspended
◆ However, 92% accounts suspended within 3 days in URL based
spam campaigns
◆ 4,581 unique URLs, 594 distinct domains
◆ 10% URLs suspended by Web Of Trust (WOT); none by Google Safe
Browsing
31
“
Can cross-platform
intelligence from Online
Social Networks be used?
32
Homogeneous Identities
Same identity across networks; levenshtein distance on usernames
33
Cross-Platform Intelligence
◆ 65 instances of homogeneous identities
◆ 52% more posts on GooglePlus; 93.3% more accounts suspended
on Twitter
◆ Intelligence propagation from Twitter to other OSNs
◆ Reducing financial loss and victims: collected friends, followers,
and likes on Facebook, GooglePlus, and YouTube
34
Cross-Platform Intelligence (I)
◆ Can save approximately 8.8M USD
▶ 21,053 friends on Facebook
▶ 11,538 followers on GooglePlus
▶ 2,816 likes on YouTube
▶ Total - 670,164 users
▶ Average cost of TechSupport spam - $290.9 per victim
▶ Total money saved - 670,164*290.9 = $8.8M
35
“Do legitimate campaigns
exist on OSNs? How are they
different from spam
campaigns?
36
Comparing Spam and Legitimate Identities
16 brands targeted like Microsoft, Facebook, Yahoo, McAfee etc.
37
Category Spam Legitimate
Number of posts 269,652 5,712
Number of unique phone numbers 1,164 279
Number of unique IDs 6,077 794
Number of suspended IDs 67,757 47
Spammers vs. Non-spammers
38
Legitimate accounts post about 1
brand while spammers promote
multiple brands
Larger lifetime of legitimate phone
number than phone numbers used in
spam campaigns
Network Characteristics
39
Non-spammers Spammers
Takeaways
◆ Cross-platform spam campaigns span across multiple
countries: top are Indonesia, USA, India, and UAE
◆ URL spammers are suspended within 3 days while 68.7%
phone-based spammers are never suspended
◆ Cross-platform intelligence can be shared across OSNs: Twitter
is able to suspend 93.3% more accounts than Facebook. Around
35, 407 victims can be protected and $8.8M be saved
◆ Spammers collude and form dense communities to expand
their reach
40
Contributions Summary
◆ Building automated frameworks to identify and characterise
phone-based spam campaigns on social channels
◆ Evaluating the effectiveness of existing state-of-art tools in
detecting spam phone numbers
◆ Mitigating phone-based spam campaigns by building
SpamDoctor, a supervised detection method to flag phone
numbers abused on OSNs.
41
Fake Registration
◆ No means of identity verification
◆ Social media accounts can be linked
◆ Similar situation for multiple
Applications like Whitepages Pro,
Contactive, Whoscall, Hello
42
Trust in Caller ID Applications
43
Spam Phone Number Coverage
◆ FTC Do-not-complaint dataset (0.001%)
▶ Information reported by consumers
▶ Do not call and robocall complaints
◆ Truecaller - 0.4%
▶ Exploiting search endpoint to crawl data
◆ MalwareBytes - 20.3%
▶ Coverage with only TechSupport campaign
https://www.ftc.gov/site-information/open-government/data-sets/do-not-call-data
44
“
How to mitigate phone based
spam campaigns?
45
Contributions Summary
◆ Building automated frameworks to identify and characterise
phone-based spam campaigns on social channels
◆ Evaluating the effectiveness of existing state-of-art tools in
detecting spam phone numbers
◆ Mitigating phone-based spam campaigns by building
SpamDoctor, a supervised detection method to flag phone
numbers abused on OSNs.
46
Revisiting Dataset
◆ Campaigns with at-least one suspended user: 3,370 / 22,390
◆ 670,257 unique user accounts, 5,593 already suspended
◆ 26,160 unique phone numbers
◆ 893,808 unique URLs
47
Gupta, S., Khattar, A., Gogia, A., Kumaraguru, P. and Chakraborty, T. Collective Classification of Spam Campaigners on Twitter: A
Hierarchical Meta-Path Based Approach. Accepted at The Web Conf 2018 (Formerly WWW Conference).
Heterogeneous Networks and Meta-Paths
Meta-Paths: Two users can be connected
via different paths
viz. user-phone-user, user-url-user,
user-phone-url-user
Collective Classification: Combined
classification of nodes
based on correlations between known
and unknown nodes
Known nodes: Already suspended users
by Twitter
48
Methodology
49
Hierarchical Meta-Path Based Score (HMPS)
Local HMPS score calculated for each campaign: HMPS value for
spammer in campaign1
can be shared by non-spammer in campaign2
50
Edge Weights
◆ W(Useri
, Phonej
): This is the weight of the edge connecting a
user and a phone number, as is measured as the ratio of tweets
propagated by Useri
containing Phonej
over all the tweets
propagated by Useri
◆ W(Useri
, URLj
): This is the weight of the edge connecting a user
and a URL, and is measured as the ratio of tweets propagated by
Useri
containing URLj
over all the tweets propagated by Useri
51
Edge Weights (I)
◆ W(Campi
, Phonej
): This is the weight of the edge connecting a
campaign and a phone number, and is measured as the ratio of
tweets containing Phonej
in Campi
over all the tweets
containing phone numbers in Campi
◆ W(Campi
, URLj
): This is the weight of the edge connecting a
campaign and a URL, and is measured as the ratio of tweets
containing URLj
in Campi
over all the tweets containing URLs in
Campi
52
HMPS (User1
)
◆ Weight between User1
and User2
,
W1
: W(User1
, Phone2
)* (User2
, Phone2
)
◆ Weight between User1
and User4
, W2
: maximum score
calculated for 2 possible meta-paths,
i.e., User1
-URL1
-User4
and User1
-Phone2
-Camp1
-URL1
-User4
;
W2
= max ([W(User1
, URL1
) * W(User4
, URL1
)],
[W(User1
, Phone2
) * W(Camp1
, Phone2
) *
W(Camp1
, URL1
) * W(User4
, URL1
)])
◆ The final HMPS of User1
, HMPS (User1
) = W1
+ W2
53
Challenges
◆ Imbalanced Dataset
◆ Manual labelling needs human efforts
◆ Individual campaigns might not have sufficient training
samples
54
Challenges
◆ Imbalanced Dataset - One class classifier!
◆ Manual labelling needs human efforts - One class classifier!
◆ Individual campaigns might not have sufficient training
samples:
▶ Active learning with feedback used
▶ Gather cues for unknown users from multiple campaigns —
21% overlapping users
55
Active Learning with Feedback
56
Selection Criterion
Given (a) a one-class classifier C, represented by the function f(x)
which, for instance x, provides the distance of x from the
classification boundary, and (b) X, a set of unlabeled instances, we
take the maximum distance among all the training samples from
the decision boundary, Tc
max
= maxx ∈X
f(x). Now, from the unknown
set Xu
, which are labeled by C, we choose those instances X’u
such
that ∀x
∈ Xu
’ : f(x) >= Tc
max
. Note that the threshold Tc
max
is specific
to a campaign
57
Comparison with Baselines
Baseline 1: Profile based features like tweets, followers, hashtags etc. [1].
Baseline 2: URL based features like number of URLs, number of words in
the URL etc.[2].
Baseline 3: Content based features like tweets, hashtags, mentions,
popularity ratio etc.[3].
[1] Fabricio Benevenuto, Gabriel Magno, Tiago Rodrigues, and Virgilio Almeida. 2010. Detecting spammers on
twitter. In Collaboration, electronic messaging, anti-abuse and spam conference (CEAS), Vol. 6. 1–12.
[2] Usman US Khan, Mazhar Ali, Assad Abbas, Samee Khan, and Albert Zomaya. 2016. Segregating Spammers
and Unsolicited Bloggers from Genuine Experts on Twitter. IEEE Transactions on Dependable and Secure
Computing (2016).
[3] Kayode Sakariyah Adewole, Nor Badrul Anuar, Amirrudin Kamsin, and Arun Kumar Sangaiah. 2017. SMSAD: a
framework for spam message and spam account detection. Multimedia Tools and Applications (2017), 1–36.
58
Evaluation
◆ Setting 1: Leave-one out cross-validation
◆ Setting 2:
▶ Human annotation
▶ convenience sampling to pick users part of multiple
campaigns
▶ 700 users sampled
59
Results
Method Feature Setting 1 Setting 2
Accuracy P R F1 AUC
Baseline 1 OSN1 0.62 0.86 0.71 0.77 0.48
Baseline 2 OSN2 0.58 0.84 0.92 0.87 0.52
Baseline 3 OSN3 0.62 0.86 0.66 0.74 0.47
Our HMPS 0.77 0.99 0.87 0.93 0.88
HMPS + OSN1 0.76 0.89 0.90 0.89 0.72
HMPS + OSN2 0.84 0.98 0.88 0.93 0.87
HMPS + OSN3 0.70 0.88 0.73 0.80 0.59
Our HMPS + OSN2 - Active Learning - 0.42 0.98 0.55 0.51
60
1-class vs. 2-class Classifier
Method Precision Recall F1-Score AUC
Baseline 1 0.68 0.69 0.65 0.50
Baseline 2 0.47 0.57 0.51 0.50
Baseline 3 0.79 0.78 0.78 0.57
HMPS + 2-class classifiers
LR 0.61 0.58 0.55 0.58
LDA 0.61 0.58 0.55 0.58
DT 0.83 0.83 0.83 0.83
NB 0.60 0.58 0.57 0.58
SVM 0.65 0.63 0.62 0.63
RF 0.83 0.82 0.82 0.82
HMPS + OSN2 0.95 0.90 0.93 0.92
61
Feedback vs. Oversampling
Oversampling + default one-class classifier
Precision Recall F1-Score AUC
Ratio = 0.20 0.90 0.64 0.64 0.59
Ratio = 0.30 0.88 0.74 0.74 0.63
Ratio = 0.50 0.81 0.71 0.68 0.58
Ratio = 0.75 0.91 0.68 0.69 0.56
Ratio = 1 0.91 0.68 0.70 0.57
Feedback + default one-class classifier
0.95 0.90 0.93 0.92
62
Contributions Summary
◆ Building automated frameworks to identify and characterise
phone-based spam campaigns on social channels
◆ Evaluating the effectiveness of existing state-of-art tools in
detecting spam phone numbers
◆ Mitigating phone-based spam campaigns by building
SpamDoctor, a supervised detection method to flag phone
numbers abused on OSNs.
63
How does this thesis help?
◆ Online Social Networks are a primary source of information
consumption by Internet users
▶ Unmoderated content; SpamDoctor provides a useful and usable
solution to fight back phone based spam attacks
◆ Bridging gap between different channels, i.e. Telephony and Web
▶ Help telecom service providers in blocking incoming and
outgoing services to these phone numbers
◆ Early spam detection on OSNs due to transfer learning
▶ Cross-platform intelligence can be shared across OSNs to
augment spam detection
64
Limitations and Future Work
◆ Address different type of campaigns differently
▶ Study spam and scam campaigns differently
◆ Utilize crowdsourcing to personalize and improve the
performance of automated techniques for spam campaign
identification
▶ Crowdsourced feedback to improve accuracy of models
◆ Explore the impact of images and cross referenced posts in OSNs
▶ Augmenting cross-platform intelligence
65
Acknowledgements
◆ Collaborators and co-authors: Dr. Payas Gupta, Prof. Mustaque
Ahamad, Manish Gupta, Dhruv Kuchhal, Abhinav Khattar, Arpit
Gogia, Gurpreet Singh, Saksham Suri
◆ Monitoring committee: Prof. Mustaque and Prof. Sambuddho
◆ Peers: Dr. Paridhi Jain, Dr. Niharika Sachdeva, Dr. Siddhartha
Asthana, Dr. Prateek Dewan, Anupama Aggarwal, Rishabh
Kaushal
◆ Members of Precog
◆ My family
66
Peer-reviewed Publications
Gupta S., and Kumaraguru, P. Emerging phishing trends and effectiveness of the anti-phishing landing
page. In Electronic Crime Research (eCrime), 2014 APWG Symposium on, pp. 36-47. IEEE, 2014.
Gupta, S., Gupta, P., Ahamad, M., and Kumaraguru, P. Know your targets: Privacy and Security
Implications in Instant Messaging Applications. Poster at 2nd NYUAD Annual Research Conference, Abu
Dhabi, 2015.
Gupta, S., Gupta, P., Ahamad, M., and Kumaraguru, P. Exploiting phone numbers and cross-application
features in targeted mobile attacks. In Proceedings of the 6th Workshop on Security and Privacy in
Smartphones and Mobile Devices, pp. 73-82. ACM, 2016.
Gupta, S. Emerging Threats Abusing Phone Numbers Exploiting Cross-Platform Features. 2016
IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).
Ph.D. Forum.
Gupta, S., Khattar, A., Gogia, A., Kumaraguru, P., and Chakraborty, T.. Collective Classification of Spam
Campaigners on Twitter: A Hierarchical Meta-Path Based Approach. In Proceedings of the 2018 World
Wide Web Conference, pp. 529-538, (WWW), 2018.
Gupta, S., Kuchhal, D., Gupta, P., Ahamad, M., Gupta, M., and Kumaraguru, P. Under the Shadow of
Sunshine: Characterizing Spam Campaigns Abusing Phone Numbers Across Online Social Networks. In
Proceedings of the 10th ACM Conference on Web Science, pp. 67-76. ACM, 2018.
Gupta, S., Bhatia, G., Suri, S., Kuchhal, D., Gupta, P., Ahamad, M., Gupta, M., and Kumaraguru, P.Angel or
Demon? Characterizing Variations Across Twitter Timeline of Technical Support Campaigners." [under
review in Journal of Web Science].
67
Thanks!
srishtig@iiitd.ac.in
http://precog.iiitd.edu.in/
@Srishti_Gupta14
68

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Identifying and Mitigating Cross-Platform Phone Number Abuse on Social Channels

  • 1. Identifying and Mitigating Cross-Platform Phone Number Abuse on Social Channels linkedin/in/srishti-gupta-627aa738 @Srishti_Gupta14 fb.com/gupta.srishti14 Committee Members Dr. Fabricio Benevenuto Dr. Pawan Goyal Dr. Sameep Mehta Dr. Ponnurangam Kumaraguru (Advisor) Srishti Gupta PhD Thesis Defense April 25, 2019 IIIT-Delhi 1
  • 2. Who am I? ◆ Research Scientist at American Express ◆ PhD student since December, 2013 - IIIT-Delhi ▶ Masters (2011 - 2013, IIIT-Delhi) ◆ Collaborations ▶ New York University (Abu Dhabi), Georgia Institute of Technology (Atlanta), Microsoft IDC (Hyderabad), Pindrop Security (Atlanta) ◆ Worked in Privacy and Security in Online Social Networks ◆ Research Interests ▶ Applied Machine Learning ▶ Natural Language Processing ▶ Web Security 2
  • 4. 4
  • 5. Keys to the Kingdom! Outgoing Spam Communication! 5
  • 6. What difference does it make? ◆ Numbers under spammer control - no spoofing ◆ Incoming services like Truecaller don’t work for outgoing spammers ◆ Difference w.r.t URLs? ▶ Medium different, more trust ▶ Minimal defense solutions unlike spam filters ▶ The propagating and damaging channel are different 6
  • 8. “ Locate spammers and take them down! 8
  • 9. Challenges ◆ Lack of useful header data ◆ Difficulty in handling audio streams ◆ Lack of ground truth ◆ Country of origin of phone numbers unknown (toll-free numbers) ◆ Temporary disposable numbers 9
  • 10. Thesis Statement Cross-platform phone-based spam campaigns can be disintegrated across social channels by identifying and mitigating spam using relational similarity that thrives on identifiable and discriminative public attributes 10
  • 11. Contributions Summary ◆ Building automated frameworks to identify and characterise phone-based spam campaigns on social channels ◆ Evaluating the effectiveness of existing state-of-art tools in detecting spam phone numbers ◆ Mitigating phone-based spam campaigns by building SpamDoctor, a supervised detection method to flag phone numbers abused on OSNs. 11
  • 13. Contributions Summary ◆ Building automated frameworks to identify and characterise phone-based spam campaigns on social channels ◆ Evaluating the effectiveness of existing state-of-art tools in detecting spam phone numbers ◆ Mitigating phone-based spam campaigns by building SpamDoctor, a supervised detection method to flag phone numbers abused on OSNs. 13
  • 14. Targeted Attacks on Over-The-Top (OTT) Messaging Applications 14 Malicious Entity: Advertisements, random contact requests Service Provider: Inefficient filtering mechanisms OTT User: Spam activities not yet seen
  • 15. System Architecture 15 Gupta, S., Gupta, P., Ahamad, M., and Kumaraguru, P. Exploiting Phone Numbers and Cross-Application Features in Targeted Mobile Attacks. Accepted at the 6th Workshop on Security and Privacy in Smartphones and Mobile Devices (SPSM), 2016
  • 16. Information Gathering ◆ Leveraged using Truecaller ◆ Information like name, address, photo URL, OSN handles (Twitter and Facebook), e-mail ◆ Facebook Graph API ◆ Public feeds, posts, albums (public sources) 16
  • 18. Success: Amazon Mechanical Turk 18 Social (69.2) > Spear (54.3) > Non-targeted (34.5)
  • 19. “Threat landscape of phone-based spam campaigns on Online Social Networks? 19
  • 20. System Architecture 20 Start Gupta, S., Kuchhal, D., Gupta, P., Ahamad, M., Gupta, M. and Kumaraguru, P. "Under the Shadow of Sunshine: Characterizing Spam Campaigns Abusing Phone Numbers Across Online Social Networks. Accepted in the 10th ACM Conference on Web Science, Amsterdam, 27-30 May 2018
  • 21. Data Collection ◆ Tweet Collection: using keywords like: “call”, “ring”, “reach”, “SMS”, “WhatsApp” etc. ◆ Data stored - phone number, posts, author details, URLs, suspended accounts’ information ◆ Google - Existing Internet Infrastructure 21
  • 22. Needle in the haystack! 22 P: Phone number; T: Tweet; U: Unigrams
  • 23. Ground Truth Creation ◆ Suspended accounts ◆ Overlap with FTC dataset ◆ Overlap with existing Truecaller services ◆ Duplicate posts by single and multiple accounts 23
  • 24. Dataset ◆ ~22M posts ▶ 22,390 campaigns ▶ 1,845,150 distinct phone numbers ▶ 3,365,017 distinct user accounts ◆ ~4.9M posts ▶ Manually verified 202 campaigns ▶ 2,346 distinct phone numbers ▶ 157,494 distinct user accounts 24
  • 25. Modus Operandi ◆ Advanced fee ◆ Selling Products ◆ Alternating beliefs (LoveGuru) ◆ Tech Support 25
  • 26. Where does Phone Spam Originate? ◆ Country code using Google libphonenumber ◆ Automated calling using ◆ Google Speech API (Audio to text) 26
  • 27. Where does Phone Spam Originate? 27
  • 28. “ How do campaigns spread across Online Social Networks? 28
  • 29. Case Study: Tech Support Campaign ◆ 43,552 posts ◆ Used toll-free numbers ◆ Majority phone numbers registered between 2014 and 2016 Feature Twitter Facebook GooglePlus YouTube Flickr Total Posts 28,984 2,151 7,830 2,850 1,737 Dis. Phone Numbers 41 33 37 39 20 Distinct User IDs 748 289 360 433 79 29
  • 30. Cross-Pollination ◆ Is particular OSN prefered? Specific pattern? 30
  • 31. Existing Web Intelligence useful? ◆ 68.7% accounts never suspended ◆ However, 92% accounts suspended within 3 days in URL based spam campaigns ◆ 4,581 unique URLs, 594 distinct domains ◆ 10% URLs suspended by Web Of Trust (WOT); none by Google Safe Browsing 31
  • 32. “ Can cross-platform intelligence from Online Social Networks be used? 32
  • 33. Homogeneous Identities Same identity across networks; levenshtein distance on usernames 33
  • 34. Cross-Platform Intelligence ◆ 65 instances of homogeneous identities ◆ 52% more posts on GooglePlus; 93.3% more accounts suspended on Twitter ◆ Intelligence propagation from Twitter to other OSNs ◆ Reducing financial loss and victims: collected friends, followers, and likes on Facebook, GooglePlus, and YouTube 34
  • 35. Cross-Platform Intelligence (I) ◆ Can save approximately 8.8M USD ▶ 21,053 friends on Facebook ▶ 11,538 followers on GooglePlus ▶ 2,816 likes on YouTube ▶ Total - 670,164 users ▶ Average cost of TechSupport spam - $290.9 per victim ▶ Total money saved - 670,164*290.9 = $8.8M 35
  • 36. “Do legitimate campaigns exist on OSNs? How are they different from spam campaigns? 36
  • 37. Comparing Spam and Legitimate Identities 16 brands targeted like Microsoft, Facebook, Yahoo, McAfee etc. 37 Category Spam Legitimate Number of posts 269,652 5,712 Number of unique phone numbers 1,164 279 Number of unique IDs 6,077 794 Number of suspended IDs 67,757 47
  • 38. Spammers vs. Non-spammers 38 Legitimate accounts post about 1 brand while spammers promote multiple brands Larger lifetime of legitimate phone number than phone numbers used in spam campaigns
  • 40. Takeaways ◆ Cross-platform spam campaigns span across multiple countries: top are Indonesia, USA, India, and UAE ◆ URL spammers are suspended within 3 days while 68.7% phone-based spammers are never suspended ◆ Cross-platform intelligence can be shared across OSNs: Twitter is able to suspend 93.3% more accounts than Facebook. Around 35, 407 victims can be protected and $8.8M be saved ◆ Spammers collude and form dense communities to expand their reach 40
  • 41. Contributions Summary ◆ Building automated frameworks to identify and characterise phone-based spam campaigns on social channels ◆ Evaluating the effectiveness of existing state-of-art tools in detecting spam phone numbers ◆ Mitigating phone-based spam campaigns by building SpamDoctor, a supervised detection method to flag phone numbers abused on OSNs. 41
  • 42. Fake Registration ◆ No means of identity verification ◆ Social media accounts can be linked ◆ Similar situation for multiple Applications like Whitepages Pro, Contactive, Whoscall, Hello 42
  • 43. Trust in Caller ID Applications 43
  • 44. Spam Phone Number Coverage ◆ FTC Do-not-complaint dataset (0.001%) ▶ Information reported by consumers ▶ Do not call and robocall complaints ◆ Truecaller - 0.4% ▶ Exploiting search endpoint to crawl data ◆ MalwareBytes - 20.3% ▶ Coverage with only TechSupport campaign https://www.ftc.gov/site-information/open-government/data-sets/do-not-call-data 44
  • 45. “ How to mitigate phone based spam campaigns? 45
  • 46. Contributions Summary ◆ Building automated frameworks to identify and characterise phone-based spam campaigns on social channels ◆ Evaluating the effectiveness of existing state-of-art tools in detecting spam phone numbers ◆ Mitigating phone-based spam campaigns by building SpamDoctor, a supervised detection method to flag phone numbers abused on OSNs. 46
  • 47. Revisiting Dataset ◆ Campaigns with at-least one suspended user: 3,370 / 22,390 ◆ 670,257 unique user accounts, 5,593 already suspended ◆ 26,160 unique phone numbers ◆ 893,808 unique URLs 47 Gupta, S., Khattar, A., Gogia, A., Kumaraguru, P. and Chakraborty, T. Collective Classification of Spam Campaigners on Twitter: A Hierarchical Meta-Path Based Approach. Accepted at The Web Conf 2018 (Formerly WWW Conference).
  • 48. Heterogeneous Networks and Meta-Paths Meta-Paths: Two users can be connected via different paths viz. user-phone-user, user-url-user, user-phone-url-user Collective Classification: Combined classification of nodes based on correlations between known and unknown nodes Known nodes: Already suspended users by Twitter 48
  • 50. Hierarchical Meta-Path Based Score (HMPS) Local HMPS score calculated for each campaign: HMPS value for spammer in campaign1 can be shared by non-spammer in campaign2 50
  • 51. Edge Weights ◆ W(Useri , Phonej ): This is the weight of the edge connecting a user and a phone number, as is measured as the ratio of tweets propagated by Useri containing Phonej over all the tweets propagated by Useri ◆ W(Useri , URLj ): This is the weight of the edge connecting a user and a URL, and is measured as the ratio of tweets propagated by Useri containing URLj over all the tweets propagated by Useri 51
  • 52. Edge Weights (I) ◆ W(Campi , Phonej ): This is the weight of the edge connecting a campaign and a phone number, and is measured as the ratio of tweets containing Phonej in Campi over all the tweets containing phone numbers in Campi ◆ W(Campi , URLj ): This is the weight of the edge connecting a campaign and a URL, and is measured as the ratio of tweets containing URLj in Campi over all the tweets containing URLs in Campi 52
  • 53. HMPS (User1 ) ◆ Weight between User1 and User2 , W1 : W(User1 , Phone2 )* (User2 , Phone2 ) ◆ Weight between User1 and User4 , W2 : maximum score calculated for 2 possible meta-paths, i.e., User1 -URL1 -User4 and User1 -Phone2 -Camp1 -URL1 -User4 ; W2 = max ([W(User1 , URL1 ) * W(User4 , URL1 )], [W(User1 , Phone2 ) * W(Camp1 , Phone2 ) * W(Camp1 , URL1 ) * W(User4 , URL1 )]) ◆ The final HMPS of User1 , HMPS (User1 ) = W1 + W2 53
  • 54. Challenges ◆ Imbalanced Dataset ◆ Manual labelling needs human efforts ◆ Individual campaigns might not have sufficient training samples 54
  • 55. Challenges ◆ Imbalanced Dataset - One class classifier! ◆ Manual labelling needs human efforts - One class classifier! ◆ Individual campaigns might not have sufficient training samples: ▶ Active learning with feedback used ▶ Gather cues for unknown users from multiple campaigns — 21% overlapping users 55
  • 56. Active Learning with Feedback 56
  • 57. Selection Criterion Given (a) a one-class classifier C, represented by the function f(x) which, for instance x, provides the distance of x from the classification boundary, and (b) X, a set of unlabeled instances, we take the maximum distance among all the training samples from the decision boundary, Tc max = maxx ∈X f(x). Now, from the unknown set Xu , which are labeled by C, we choose those instances X’u such that ∀x ∈ Xu ’ : f(x) >= Tc max . Note that the threshold Tc max is specific to a campaign 57
  • 58. Comparison with Baselines Baseline 1: Profile based features like tweets, followers, hashtags etc. [1]. Baseline 2: URL based features like number of URLs, number of words in the URL etc.[2]. Baseline 3: Content based features like tweets, hashtags, mentions, popularity ratio etc.[3]. [1] Fabricio Benevenuto, Gabriel Magno, Tiago Rodrigues, and Virgilio Almeida. 2010. Detecting spammers on twitter. In Collaboration, electronic messaging, anti-abuse and spam conference (CEAS), Vol. 6. 1–12. [2] Usman US Khan, Mazhar Ali, Assad Abbas, Samee Khan, and Albert Zomaya. 2016. Segregating Spammers and Unsolicited Bloggers from Genuine Experts on Twitter. IEEE Transactions on Dependable and Secure Computing (2016). [3] Kayode Sakariyah Adewole, Nor Badrul Anuar, Amirrudin Kamsin, and Arun Kumar Sangaiah. 2017. SMSAD: a framework for spam message and spam account detection. Multimedia Tools and Applications (2017), 1–36. 58
  • 59. Evaluation ◆ Setting 1: Leave-one out cross-validation ◆ Setting 2: ▶ Human annotation ▶ convenience sampling to pick users part of multiple campaigns ▶ 700 users sampled 59
  • 60. Results Method Feature Setting 1 Setting 2 Accuracy P R F1 AUC Baseline 1 OSN1 0.62 0.86 0.71 0.77 0.48 Baseline 2 OSN2 0.58 0.84 0.92 0.87 0.52 Baseline 3 OSN3 0.62 0.86 0.66 0.74 0.47 Our HMPS 0.77 0.99 0.87 0.93 0.88 HMPS + OSN1 0.76 0.89 0.90 0.89 0.72 HMPS + OSN2 0.84 0.98 0.88 0.93 0.87 HMPS + OSN3 0.70 0.88 0.73 0.80 0.59 Our HMPS + OSN2 - Active Learning - 0.42 0.98 0.55 0.51 60
  • 61. 1-class vs. 2-class Classifier Method Precision Recall F1-Score AUC Baseline 1 0.68 0.69 0.65 0.50 Baseline 2 0.47 0.57 0.51 0.50 Baseline 3 0.79 0.78 0.78 0.57 HMPS + 2-class classifiers LR 0.61 0.58 0.55 0.58 LDA 0.61 0.58 0.55 0.58 DT 0.83 0.83 0.83 0.83 NB 0.60 0.58 0.57 0.58 SVM 0.65 0.63 0.62 0.63 RF 0.83 0.82 0.82 0.82 HMPS + OSN2 0.95 0.90 0.93 0.92 61
  • 62. Feedback vs. Oversampling Oversampling + default one-class classifier Precision Recall F1-Score AUC Ratio = 0.20 0.90 0.64 0.64 0.59 Ratio = 0.30 0.88 0.74 0.74 0.63 Ratio = 0.50 0.81 0.71 0.68 0.58 Ratio = 0.75 0.91 0.68 0.69 0.56 Ratio = 1 0.91 0.68 0.70 0.57 Feedback + default one-class classifier 0.95 0.90 0.93 0.92 62
  • 63. Contributions Summary ◆ Building automated frameworks to identify and characterise phone-based spam campaigns on social channels ◆ Evaluating the effectiveness of existing state-of-art tools in detecting spam phone numbers ◆ Mitigating phone-based spam campaigns by building SpamDoctor, a supervised detection method to flag phone numbers abused on OSNs. 63
  • 64. How does this thesis help? ◆ Online Social Networks are a primary source of information consumption by Internet users ▶ Unmoderated content; SpamDoctor provides a useful and usable solution to fight back phone based spam attacks ◆ Bridging gap between different channels, i.e. Telephony and Web ▶ Help telecom service providers in blocking incoming and outgoing services to these phone numbers ◆ Early spam detection on OSNs due to transfer learning ▶ Cross-platform intelligence can be shared across OSNs to augment spam detection 64
  • 65. Limitations and Future Work ◆ Address different type of campaigns differently ▶ Study spam and scam campaigns differently ◆ Utilize crowdsourcing to personalize and improve the performance of automated techniques for spam campaign identification ▶ Crowdsourced feedback to improve accuracy of models ◆ Explore the impact of images and cross referenced posts in OSNs ▶ Augmenting cross-platform intelligence 65
  • 66. Acknowledgements ◆ Collaborators and co-authors: Dr. Payas Gupta, Prof. Mustaque Ahamad, Manish Gupta, Dhruv Kuchhal, Abhinav Khattar, Arpit Gogia, Gurpreet Singh, Saksham Suri ◆ Monitoring committee: Prof. Mustaque and Prof. Sambuddho ◆ Peers: Dr. Paridhi Jain, Dr. Niharika Sachdeva, Dr. Siddhartha Asthana, Dr. Prateek Dewan, Anupama Aggarwal, Rishabh Kaushal ◆ Members of Precog ◆ My family 66
  • 67. Peer-reviewed Publications Gupta S., and Kumaraguru, P. Emerging phishing trends and effectiveness of the anti-phishing landing page. In Electronic Crime Research (eCrime), 2014 APWG Symposium on, pp. 36-47. IEEE, 2014. Gupta, S., Gupta, P., Ahamad, M., and Kumaraguru, P. Know your targets: Privacy and Security Implications in Instant Messaging Applications. Poster at 2nd NYUAD Annual Research Conference, Abu Dhabi, 2015. Gupta, S., Gupta, P., Ahamad, M., and Kumaraguru, P. Exploiting phone numbers and cross-application features in targeted mobile attacks. In Proceedings of the 6th Workshop on Security and Privacy in Smartphones and Mobile Devices, pp. 73-82. ACM, 2016. Gupta, S. Emerging Threats Abusing Phone Numbers Exploiting Cross-Platform Features. 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). Ph.D. Forum. Gupta, S., Khattar, A., Gogia, A., Kumaraguru, P., and Chakraborty, T.. Collective Classification of Spam Campaigners on Twitter: A Hierarchical Meta-Path Based Approach. In Proceedings of the 2018 World Wide Web Conference, pp. 529-538, (WWW), 2018. Gupta, S., Kuchhal, D., Gupta, P., Ahamad, M., Gupta, M., and Kumaraguru, P. Under the Shadow of Sunshine: Characterizing Spam Campaigns Abusing Phone Numbers Across Online Social Networks. In Proceedings of the 10th ACM Conference on Web Science, pp. 67-76. ACM, 2018. Gupta, S., Bhatia, G., Suri, S., Kuchhal, D., Gupta, P., Ahamad, M., Gupta, M., and Kumaraguru, P.Angel or Demon? Characterizing Variations Across Twitter Timeline of Technical Support Campaigners." [under review in Journal of Web Science]. 67