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Exposing and Mitigating Cross-Channel Abuse That Exploits the Converged Communications Infrastructure

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EXPOSING AND MITIGATING CROSS-CHANNEL ABUSE THAT EXPLOITS THE CONVERGED COMMUNICATIONS INFRASTRUCTURE A Thesis Presented to The Academic Faculty by Bharat Ramakrishnan Srinivasan In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the School of Computer Science, College of Computing Georgia Institute of Technology December 2017 Copyright © 2017 by Bharat Ramakrishnan Srinivasan EXPOSING AND MITIGATING CROSS-CHANNEL ABUSE THAT EXPLOITS THE CONVERGED COMMUNICATIONS INFRASTRUCTURE Approved by: Professor Mustaque Ahamad, Advisor Professor Mostafa Ammar School of Computer Science School of Computer Science Georgia Institute of Technology Georgia Institute of Technology Professor Manos Antonakakis Professor Nick Nikiforakis School of Electrical and Computer Department of Computer Science Engineering Stony Brook University Georgia Institute of Technology Professor Wenke Lee Date Approved: 27 October 2017 School of Computer Science Georgia Institute of Technology To my parents. iii ACKNOWLEDGEMENTS First and foremost I would like to express my sincere gratitude and appreciation to my advisor Prof. Mustaque Ahamad. He has provided me with unwavering support throughout my Ph.D. journey. I would also like to thank other members of the dissertation committee, Prof. Manos Antonakakis, Prof. Wenke Lee, Prof. Mostafa Ammar and Prof. Nick Niki- forakis for serving on my committee and for their suggestions in improving this dis- sertation work. My collaboration with both Manos and Nick significantly improved the quality of work presented in this dissertation. I am also grateful to the Institute of Security and Privacy (IISP) support staff, Elizabeth Ndongi and Trinh Doan for being ever helpful and taking care of all the administrative paperwork. I would also to like to thank Paul Royal for lending a helping hand in my research especially during the initial years. A special mention goes to Prof. H. Venkateswaran for being a champion of Ph.D. students in the School of Computer Science and providing me with sound advice whenever needed. I was fortuntate to work with and know Satya Lokam from Microsoft Research, Shachi Sharma and Shalini Kapoor from IBM Research, Sarat Babu and Mahesh U. Patil from Center of Development of Advanced Computing (CDAC). I am thankful to each of them for their mentorship. I would also like to thank Frederick Lee from Twilio Inc. and James Dolph from Salesforce.com for giving me the opportunity to work on interesting security problems faced by industry during my internships in the respective organizations. My time at Georgia Tech was made enjoyable in large parts due to my labmates, friends and mentors I gained here. A special mention to Sudarsun Kannan, Anand iv Louis, Dipanjan Sengupta, Vijay Balasubramanian, Musheer Ahmed, Payas Gupta, Simon Chung, Yacin Nadji, Byoungyoung Lee, Yeongjin Jang, Kadappan Panayap- pan, Rajan Damle, Ravi Mangal, Shauvik Roy Choudhary, Monjur Alam, Natesh Srinivasan, Sanidhya Kashyap, Pranay Swar, Aditi Ghag, Shardul, Akhil Gupta, Suneet Gupta, Dhanesh Gandhi, Scott Freeman and Leroy Baker. My sincere gratitude also goes to friends from my alma matter, BITS Pilani and DPS R.K. Puram - Bharat Raju, Dev Basu, Abhinay Pochiraju, Amrit Moharana, Rahul Rai, Deepak Sharma, Kusumakar Althi, Shruti Balaji, Dharashree Panda and Dheeraj Sanka who have all been my well wishers. Finally, I am indebted to my family for their unconditional love and support. I have greatly admired my father and mother in many ways, for their problem solving skills, hard work, persistence, and indomitable spirit. A special shout out to my dear sister for being younger yet wiser than me and motivating me to purse interests outside of work. I was also blessed by my maternal and paternal grandparents who lived a simple life in spite of the prestigious lineage they inherited from the Chakravarti Rajagopalachari (Rajaji) and Kasturiranga Santhanam families, both towering figures in the Indian Independence movement. I owe many thanks to my aunt, Sita, my uncle, Ramby, Divya, Arjun, Ravi and Shakhila for making me feel at home the moment I stepped into the United States as a student. Last but not the least, I would like to thank my wife for bringing a breath of fresh air in my life and waiting patiently for me to complete this dissertation. v TABLE OF CONTENTS DEDICATION iii ACKNOWLEDGEMENTS iv LIST OF TABLES viii LIST OF FIGURES ix SUMMARY xi I INTRODUCTION 1 1.1 Background and Motivation . 1 1.2 Cross-Channel Abuse in Context . 3 1.3 Dissertation Statement . 4 1.4 Contributions . 5 1.5 Dissertation Overview . 6 II RELATED WORK 8 2.1 Telephony Abuse . 8 2.1.1 Messaging Abuse . 9 2.1.2 Voice Abuse . 10 2.2 Internet Abuse related to Cross-Channel Applications . 11 2.2.1 DNS Replication and IP Reputation . 11 2.2.2 URL, Domain and Website Abuse . 12 2.2.3 Search and Advertisement Abuse . 13 2.3 Summary . 14 III CHURN: CROSS-CHANNEL MESSAGING ATTRIBUTION EN- GINE 15 3.1 Context and Contributions . 16 3.2 Background . 18 3.3 Cross-Channel Attribution Engine . 20 3.3.1 Data Collection Module . 21 3.3.2 DS: Data Sanitization Module . 23 3.3.3 HCL: Hierarchical Clustering Module . 25 3.3.4 AM: Cluster Attribution Module . 29 3.4 Results . 30 vi 3.4.1 Datasets . 30 3.4.2 Clustering Results . 33 3.5 Case Studies . 38 3.6 Summary . 41 IV X-TSS: MEASURING SEARCH AND AD-BASED CROSS-CHANNEL ABUSE IN TECHNICAL SUPPORT SCAMS 45 4.1 Context and Contributions . 46 4.2 Methodology . 50 4.2.1 Search Phrase Seed Generator . 51 4.2.2 Search Engine Crawler (SEC) Module . 53 4.2.3 Active Crawler Module (ACM) . 54 4.2.4 Categorization Module . 56 4.2.5 Network Amplification Module . 59 4.2.6 Clustering Module . 61 4.3 Results . 62 4.3.1 Dataset Summary . 63 4.3.2 Search Phrases Popularity and SR Rankings . 67 4.3.3 Network Amplification Efficacy . 72 4.3.4 Domain Infrastructure Analysis . 74 4.3.5 Campaigns . 78 4.4 Case Studies . 82 4.4.1 Black Hat SEO TSS Campaign . 82 4.4.2 Hijacking the Browser to Serve TSS ADs: Goentry.com . 84 4.5 Comparison with ROBOVIC . 86 4.6 Summary . 87 V CROSS-CHANNEL INTELLIGENCE SHARING 89 5.1 Sharing of Intelligence: Telephony to Internet . 90 5.1.1 Telephony abuse and its relation to Internet blacklists . 90 5.1.2 Potential benefits of sharing intelligence . 91 5.2 Sharing of Intelligence: Internet to Telephony . 93 5.2.1 Internet abuse and its relation to Telephony Blacklists . 93 5.2.2 Potential benefits of sharing intelligence . 95 5.3 Disseminating Cross-Channel Abuse Intelligence . 98 5.4 Summary . 99 VI CONCLUSION AND FUTURE WORK 101 6.1 Dissertation Summary and Contributions . 101 6.2 Limitations . 103 6.3 Future Work . 103 REFERENCES 105 VITA 113 vii LIST OF TABLES 1 Abuse divided into three classes: Internet channel only, telephony channel only and cross-channel. 4 2 Confusion matrix for the parking classifier. 25 3 Summary of collected datasets. 31 4 Representative sample of attributed clusters at various levels of the clustering hierarchy. Apart from the above and the case studies, we dis- covered campaigns related to selling drugs, adult content, free cruises, fake deals and many more. 35 5 CHURN evaluation based on ground truth with different system pa- rameter settings across all epochs. 37 6 Summary and examples of generated n-grams related to technical sup- port scams. 52 7 Confusion matrix for the TSS classifier on the testing set. 59 8 Categorization of Search Results. ∗Includes FakeCall, FakeBSOD, Tech- Brolo etc. 64 9 Most abused top-level domains (TLDs) used in final-landing TSS web- sites. 75 10 Overlap between final landing TSS domains with popular public black- lists. +includes Malware Domains List, sans, Spamhaus, itmate, sagadc, hphosts, abuse.ch and Malc0de DB. 77 11 Selected large campaigns, as identified by the clustering module. 81 12 Some of the support and final-landing domains seen in the largest TSS campaign from our dataset. 83 13 Coverage in telephony to Internet intelligence sharing scenario. +includes Malware Domains List, sans, Spamhaus, itmate, sagadc, hphosts, abuse.ch and Malc0de DB. 91 14 Coverage in Internet to telephony intelligence sharing scenario. 94 15 Top 20 most abused toll free number providers. 97 viii LIST OF FIGURES 1 Illustration of the cross-channel abuse ecosystem, delivery mechanisms and potential observation points. 19 2 CHURN: The cross-channel messaging attribution engine. 21 3 CDF of Resource Records per IP with cut-off threshold θp. 24 4 Temporal characteristics of collected datasets. 32 5 The eCDF of the lifetime of all domains showing long-lived SMS-spam domains. 32 6 HCL Thresholds . 34 7 A radial dendrogram plot illustrating the output from the hierarchical clustering module for a single epoch. 36 8 Three Campaigns: Financial Freedom, Payday and Gift Card. For each we show (8(a)–8(c)) web pages rendered on a mobile browser. 39 9 Three Campaigns: Financial Freedom, Payday and Gift Card. For each we show (9(a)–9(c)) daily lookup volumes according to our pDNS database. 42 10 Three Campaigns: Financial Freedom, Payday and Gift Card. For each we show (10(a)–10(c)) eCDF of the lifetime of the domains seen. 43 11 Three Campaigns: Financial Freedom, Payday and Gift Card. For each we show (11(a)–11(c)) 3D view of campaigns based on time, popularity and network infrastructure (IPs binned by /24 prefix). 44 12 Timeline of some news events related to search-based technical support scams (TSS). 46 13 Goentry.com search results on July 1st, 2016. 47 14 Bing.com search results on Feb 2nd, 2017. 48 15 X-TSS: The Cross-Channel TSS threat collection and analysis system.
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