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Exposing Search and Advertisement Abuse Tactics and Infrastructure of Technical Support Scammers

Published: 23 April 2018 Publication History

Abstract

Technical Support Scams (TSS), which combine online abuse with social engineering over the phone channel, have persisted despite several law enforcement actions. Although recent research has provided important insights into TSS, these scams have now evolved to exploit ubiquitously used online services such as search and sponsored advertisements served in response to search queries. We use a data-driven approach to understand search-and-ad abuse by TSS to gain visibility into the online infrastructure that facilitates it. By carefully formulating tech support queries with multiple search engines, we collect data about both the support infrastructure and the websites to which TSS victims are directed when they search online for tech support resources. We augment this with a DNS-based amplification technique to further enhance visibility into this abuse infrastructure. By analyzing the collected data, we provide new insights into search-and-ad abuse by TSS and reinforce some of the findings of earlier research. Further, we demonstrate that tech support scammers are (1) successful in getting major as well as custom search engines to return links to websites controlled by them, and (2) they are able to get ad networks to serve malicious advertisements that lead to scam pages. Our study period of approximately eight months uncovered over 9,000 TSS domains, of both passive and aggressive types, with minimal overlap between sets that are reached via organic search results and sponsored ads. Also, we found over 2,400 support domains which aid the TSS domains in manipulating organic search results. Moreover, to our surprise, we found very little overlap with domains that are reached via abuse of domain parking and URL-shortening services which was investigated previously. Thus, investigation of search-and-ad abuse provides new insights into TSS tactics and helps detect previously unknown abuse infrastructure that facilitates these scams.

References

[1]
Online. 800notes - Directory of UNKNOWN Callers. http://800notes.com/. (Online).
[2]
Online. abuse.ch - the swiss security blog. https://www.abuse.ch/. (Online).
[3]
Online. Active DNS Project. https://www.activednsproject.org/. (Online).
[4]
Online. Alexa Topsites. http://www.alexa.com/topsites. (Online).
[5]
Online. Bad Ads Trend Alert: Shining a Light on Tech Support Advertising Scams. http://bit.ly/2y2rbnq. (Online).
[6]
Online. BeautifulSoup. https://pypi.python.org/pypi/beautifulsoup4. (Online).
[7]
Online. Bing Ads bans ads from third-party tech support services. http://search engineland.com/bing-bans-third-party-tech-support-ads-249356. (Online).
[8]
Online. Bing brings in blanket ban on online tech support ads. https://goo.gl/6b gPFF. (Online).
[9]
Online. Bing Search API. http://datamarket.azure.com/dataset/bing/search. (Online).
[10]
Online. Block search indexing with 'noindex'. https://support.google.com/web masters/answer/93710?hl=en. (Online).
[11]
Online. FTC - Tech Support Scams. http://bit.ly/1XIF9RV. (Online).
[12]
Online. FTC Charges Tech Support Companies With Using Deceptive Pop-Up Ads to Scare Consumers Into Purchasing Unneeded Services. https://www.ftc.gov/news-events/press-releases/2016/10/ftc-charges-t ech-support-companies-using-deceptive-pop-ads-scare. (Online).
[13]
Online. FTC Obtains Settlements from Operators of Tech Support Scams. https://www.ftc.gov/news-events/press-releases/2017/10/ftc-obtains-set tlements-operators-tech-support-scams. (Online).
[14]
Online. Geek Squad Services - Best Buy. https://goo.gl/s7lWlq. (Online).
[15]
Online. Google Custom Search. https://goo.gl/GyU7zP. (Online).
[16]
Online. Google Safe Browsing. https://goo.gl/d1spJ. (Online).
[17]
Online. hphosts. http://www.hosts-file.net/. (Online).
[18]
Online. Indian police arrest alleged ringleader of IRS scam. http://money.cnn.co m/2017/04/09/news/tax-scam-india-arrest-ringleader/. (Online).
[19]
Online. India's Call-Center Talents Put to a Criminal Use: Swindling Americans. http://nyti.ms/2xpFv8C. (Online).
[20]
Online. I.T. Mate Product Support. http://support.it-mate.co.uk/. (Online).
[21]
Online. Keyword Planner. https://adwords.google.com/KeywordPlanner. (Online).
[22]
Online. Malc0de database. http://malc0de.com/database/. (Online).
[23]
Online. Malware Domain List. https://www.malwaredomainlist.com/. (Online).
[24]
Online. Malwarebytes Lab. https://blog.malwarebytes.com/tech-support-scams/. (Online).
[25]
Online. N-Grams. http://stanford.io/29zsjAy. (Online).
[26]
Online. PhantomJS. http://phantomjs.org/. (Online).
[27]
Online. Python language bindings for Selenium WebDriver. https://pypi.python. org/pypi/selenium. (Online).
[28]
Online. sagadc summary. http://dns-bh.sagadc.org/. (Online).
[29]
Online. Scare and sell: Here's how an Indian call centre cheated foreign computer owners. http://bit.ly/2oj2Rpz. (Online).
[30]
Online. Searching For 'Facebook Customer Service' Can Lead To A Scam. http: //n.pr/2kex6vU. (Online).
[31]
Online. SPAMHaus Blocklist. https://www.spamhaus.org/lookup/. (Online).
[32]
Online. Suspicious domains - sans internet storm center. https://isc.sans.edu/sus picious_domains.html. (Online).
[33]
Online. Tech support scams persist with increasingly crafty techniques. https: //goo.gl/cHHPDI. (Online).
[34]
Online. Tech support scams remain at the top of the list of bad actors that search engines have to keep fighting. http://selnd.com/24jskRr. (Online).
[35]
Online. Two Massive Tech Support Scams Shutdown by State of Florida, FTC. https://trustinads.org/2014/11/two-massive-tech-support-scams-shutd own-by-state-of-florida-f tc/. (Online).
[36]
Online. VirusTotal. https://www.virustotal.com/. (Online).
[37]
David S Anderson, Chris Fleizach, Stefan Savage, and Geoffrey M Voelker. Spamscatter: Characterizing internet scam hosting infrastructure.
[38]
Manos Antonakakis, Roberto Perdisci, David Dagon, Wenke Lee, and Nick Feamster. USENIX Security 2010. Building a Dynamic Reputation System for DNS.
[39]
Leyla Bilge, Engin Kirda, Christopher Kruegel, and Marco Balduzzi. NDSS 2011. EXPOSURE: Finding Malicious Domains Using Passive DNS Analysis.
[40]
Nathaniel Boggs, Wei Wang, Suhas Mathur, Baris Coskun, and Carol Pincock. ACSAC 2013. Discovery of Emergent Malicious Campaigns in Cellular Networks.
[41]
Andrei Costin, Jelena Isacenkova, Marco Balduzzi, Aurélien Francillon, and Davide Balzarotti. International Conference on Privacy, Security and Trust (PST) 2013. The role of phone numbers in understanding cyber-crime schemes.
[42]
Joe DeBlasio, Saikat Guha, Geoffrey M. Voelker, and Alex C. Snoeren. 2017. Exploring the Dynamics of Search Advertiser Fraud. In Proceedings of the 2017 Internet Measurement Conference (IMC '17). ACM, New York, NY, USA, 157--170.
[43]
Jr. Forney, G.D. 1973. The viterbi algorithm. Proc. IEEE 61, 3 (March 1973), 268--278.
[44]
Chris Grier, Kurt Thomas, Vern Paxson, and Chao Michael Zhang. ACM CCS 2010. @spam: the underground on 140 characters or less.
[45]
Payas Gupta, Bharat Srinivasan, Vijay Balasubramaniyan, and Mustaque Ahamad. 2015. Phoneypot: Data-driven Understanding of Telephony Threats. In 22nd Annual Network and Distributed System Security Symposium, NDSS 2015, San Diego, California, USA, February 8--11, 2015. The Internet Society. http://bit.ly/2wM1jff
[46]
Jelena Isacenkova, Olivier Thonnard, Andrei Costin, Aurélien Francillon, and Davide Balzarotti. EURASIP J. Information Security 2014. Inside the SCAM Jungle: A Closer Look at 419 Scam Email Operations. (EURASIP J. Information Security 2014).
[47]
Nan Jiang, Yu Jin, Ann Skudlark, and Zhi-Li Zhang. USENIX Security 2013. Greystar: Fast and Accurate Detection of SMS Spam Numbers in Large Cellular Networks Using Grey Phone Space.
[48]
Shalini Kapoor, Shachi Sharma, and Bharat Srinivasan. 2014. Clustering devices in an internet of things ('IoT'). (March 11 2014). US Patent 8,671,099.
[49]
Shalini Kapoor, Shachi Sharma, and Bharat Ramakrishnan Srinivasan. 2013. Attribute-based identification schemes for objects in internet of things. (July 23 2013). US Patent 8,495,072.
[50]
Nektarios Leontiadis, Tyler Moore, and Nicolas Christin. USENIX Security 2011. Measuring and Analyzing Search-redirection Attacks in the Illicit Online Prescription Drug Trade.
[51]
Suqi Liu, Ian Foster, Stefan Savage, Geoffrey M. Voelker, and Lawrence K. Saul. 2015. Who is .Com?: Learning to Parse WHOIS Records. In Proceedings of the 2015 Internet Measurement Conference (IMC '15). ACM.
[52]
Christopher D Manning and Hinrich Schütze. 1999. Foundations of statistical natural language processing. Vol. 999. MIT Press.
[53]
Najmeh Miramirkhani, Oleksii Starov, and Nick Nikiforakis. NDSS 2017. Dial One for Scam: A Large-Scale Analysis of Technical Support Scams.
[54]
Tyler Moore and Richard Clayton. 2007. Examining the impact of website takedown on phishing. In Proceedings of the anti-phishing working groups 2nd annual eCrime researchers summit. ACM, 1--13.
[55]
Marti Motoyama, Kirill Levchenko, Chris Kanich, Damon McCoy, Geoffrey M Voelker, and Stefan Savage. 2010. Re: CAPTCHAs-Understanding CAPTCHASolving Services in an Economic Context. In USENIX Security Symposium.
[56]
Ilona Murynets and Roger Piqueras Jover. IMC 2012. Crime scene investigation: SMS spam data analysis.
[57]
Youngsam Park, Jackie Jones, Damon McCoy, Elaine Shi, and Markus Jakobsson. NDSS 2014. Scambaiter: Understanding targeted nigerian scams on craigslist.
[58]
Dan Pelleg, Andrew W Moore, et al. ICML 2000. X-means: Extending K-means with Efficient Estimation of the Number of Clusters.
[59]
Iasonas Polakis, Thanasis Petsas, Evangelos P. Markatos, and Spyros Antonatos. NDSS 2010. A Systematic Characterization of IM Threats using Honeypots.
[60]
Merve Sahin, Marc Relieu, and Aurélien Francillon. SOUPS 2017. Using chatbots against voice spam: Analyzing Lenny effectiveness.
[61]
Gerard Salton and Michael J. McGill. 1986. Introduction to Modern Information Retrieval. McGraw-Hill, Inc., New York, NY, USA.
[62]
Shachi Sharma, Shalini Kapoor, Bharat R. Srinivasan, and Mayank S. Narula. 2011. HiCHO: Attributes Based Classification of Ubiquitous Devices. In Mobile and Ubiquitous Systems: Computing, Networking, and Services - 8th International ICST Conference, MobiQuitous 2011, Copenhagen, Denmark, December 6--9, 2011, Revised Selected Papers (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering), Alessandro Puiatti and Tao Gu (Eds.), Vol. 104. Springer, 113--125.
[63]
Craig A. Shue, Andrew J. Kalafut, and Minaxi Gupta. 2007. The Web is Smaller Than It Seems. In Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement (IMC '07). ACM, New York, NY, USA, 123--128.
[64]
Kyle Soska and Nicolas Christin. USENIX Security 2015. Measuring the Longitudinal Evolution of the Online Anonymous Marketplace Ecosystem.
[65]
Bharat Srinivasan, Payas Gupta, Manos Antonakakis, and Mustaque Ahamad. 2016. Understanding Cross-Channel Abuse with SMS-Spam Support Infrastructure Attribution. In Computer Security - ESORICS 2016 - 21st European Symposium on Research in Computer Security, Heraklion, Greece, September 26--30, 2016, Proceedings, Part I (Lecture Notes in Computer Science), Ioannis G. Askoxylakis, Sotiris Ioannidis, Sokratis K. Katsikas, and Catherine A. Meadows (Eds.), Vol. 9878. Springer, 3--26.
[66]
Bharat Ramakrishnan Srinivasan. 2017. Exposing and Mitigating Cross-Channel Abuse that Exploits the Converged Communications Infrastructure. Ph.D. Dissertation. Georgia Institute of Technology.
[67]
Kurt Thomas, Chris Grier, Justin Ma, Vern Paxson, and Dawn Song. IEEE Symposium on Security and Privacy 2011. Design and Evaluation of a Real-Time URL Spam Filtering Service.
[68]
Kurt Thomas, Dmytro Iatskiv, Elie Bursztein, Tadek Pietraszek, Chris Grier, and Damon McCoy. ACM CCS 2014. Dialing back abuse on phone verified accounts.
[69]
Michael E Wall, Andreas Rechtsteiner, and Luis M Rocha. 2003. Singular value decomposition and principal component analysis. In A practical approach to microarray data analysis. Springer, 91--109.

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      cover image ACM Other conferences
      WWW '18: Proceedings of the 2018 World Wide Web Conference
      April 2018
      2000 pages
      ISBN:9781450356398
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      • IW3C2: International World Wide Web Conference Committee

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      International World Wide Web Conferences Steering Committee

      Republic and Canton of Geneva, Switzerland

      Publication History

      Published: 23 April 2018

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      Author Tags

      1. advertisement abuse
      2. cyber crime
      3. omni/cross-channel scams
      4. search engine abuse
      5. social engineering
      6. tech support
      7. telephony fraud

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      • Research-article

      Funding Sources

      • Air Force Research Laboratory/Defense Advanced Research Projects Agency (DARPA)
      • US Department of Commerce
      • Office of Naval Research (ONR)
      • National Science Foundation (NSF)

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      WWW '18
      Sponsor:
      • IW3C2
      WWW '18: The Web Conference 2018
      April 23 - 27, 2018
      Lyon, France

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      WWW '18 Paper Acceptance Rate 170 of 1,155 submissions, 15%;
      Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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      • (2024)Internet-Based Social Engineering Psychology, Attacks, and Defenses: A SurveyProceedings of the IEEE10.1109/JPROC.2024.3379855112:3(210-246)Online publication date: Mar-2024
      • (2024)Threat of Technical Support Scams in Japan2024 IEEE Conference on Dependable and Secure Computing (DSC)10.1109/DSC63325.2024.00024(115-122)Online publication date: 6-Nov-2024
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