SoK: Everyone Hates Robocalls: A Survey of Techniques Against Telephone Spam

Tu, Huahong; Doupé, Adam; Zhao, Ziming; Ahn, Gail-Joon

doi:10.1109/sp.2016.27

Cited by 60 publications

(44 citation statements)

References 51 publications

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“…Though we are not aware of other work that has investigated technical support scams, we argue that these scams are a crossover between traditional scareware, and scams perpetrated over the telephone [43] instead of over the Internet, such as vishing (Voice Phishing). In a more general sense, our paper belongs to the literature studying cybercrime and underground ecosystems aiming to shine light on hidden mechanisms, affiliate structures, infrastructure abuse, and possible technical and economical disruptions [7], [27], [44]- [47].…”

Section: Related Workmentioning

confidence: 90%

Dial One for Scam: A Large-Scale Analysis of Technical Support Scams

Miramirkhani¹,

Starov²,

Nikiforakis³

2017

Proceedings 2017 Network and Distributed System Security Symposium

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Abstract-In technical support scams, cybercriminals attempt to convince users that their machines are infected with malware and are in need of their technical support. In this process, the victims are asked to provide scammers with remote access to their machines, who will then "diagnose the problem", before offering their support services which typically cost hundreds of dollars. Despite their conceptual simplicity, technical support scams are responsible for yearly losses of tens of millions of dollars from everyday users of the web.In this paper, we report on the first systematic study of technical support scams and the call centers hidden behind them. We identify malvertising as a major culprit for exposing users to technical support scams and use it to build an automated system capable of discovering, on a weekly basis, hundreds of phone numbers and domains operated by scammers. By allowing our system to run for more than 8 months we collect a large corpus of technical support scams and use it to provide insights on their prevalence, the abused infrastructure, the illicit profits, and the current evasion attempts of scammers. Finally, by setting up a controlled, IRB-approved, experiment where we interact with 60 different scammers, we experience first-hand their social engineering tactics, while collecting detailed statistics of the entire process. We explain how our findings can be used by law-enforcing agencies and propose technical and educational countermeasures for helping users avoid being victimized by technical support scams.

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Section: Related Workmentioning

confidence: 90%

Dial One for Scam: A Large-Scale Analysis of Technical Support Scams

Miramirkhani¹,

Starov²,

Nikiforakis³

2017

Proceedings 2017 Network and Distributed System Security Symposium

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“…Several works describe telephone spam ecosystem and provide high level evaluation for the existing techniques [29], [33]. These works highlight the requirements on designing effective malicious call prevention approaches, while our work provides a concrete solution.…”

Section: B Telephony Malicious Call Analysismentioning

confidence: 99%

A Machine Learning Approach to Prevent Malicious Calls over Telephony Networks

Liu

et al. 2018

2018 IEEE Symposium on Security and Privacy (SP)

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Malicious calls, i.e., telephony spams and scams, have been a long-standing challenging issue that causes billions of dollars of annual financial loss worldwide. This work presents the first machine learning-based solution without relying on any particular assumptions on the underlying telephony network infrastructures.The main challenge of this decade-long problem is that it is unclear how to construct effective features without the access to the telephony networks' infrastructures. We solve this problem by combining several innovations. We first develop a TouchPal user interface on top of a mobile App to allow users tagging malicious calls. This allows us to maintain a large-scale call log database. We then conduct a measurement study over three months of call logs, including 9 billion records. We design 29 features based on the results, so that machine learning algorithms can be used to predict malicious calls. We extensively evaluate different state-of-the-art machine learning approaches using the proposed features, and the results show that the best approach can reduce up to 90% unblocked malicious calls while maintaining a precision over 99.99% on the benign call traffic. The results also show the models are efficient to implement without incurring a significant latency overhead. We also conduct ablation analysis, which reveals that using 10 out of the 29 features can reach a performance comparable to using all features.

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“…Similar to online abuse and scams, telephony abuse has grown considerably and high volume scams have proliferated over the phone channel [61], [65]. Security researchers have explored such abuse and specific scams.…”

Section: Related Workmentioning

confidence: 99%

Towards Measuring the Effectiveness of Telephony Blacklists

Pandit

Perdisci

Ahamad

et al. 2018

Proceedings 2018 Network and Distributed System Security Symposium

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The convergence of telephony with the Internet has led to numerous new attacks that make use of phone calls to defraud victims. In response to the increasing number of unwanted or fraudulent phone calls, a number of call blocking applications have appeared on smartphone app stores, including a recent update to the default Android phone app that alerts users of suspected spam calls. However, little is known about the methods used by these apps to identify malicious numbers, and how effective these methods are in practice. In this paper, we are the first to systematically investigate multiple data sources that may be leveraged to automatically learn phone blacklists, and to explore the potential effectiveness of such blacklists by measuring their ability to block future unwanted phone calls. Specifically, we consider four different data sources: user-reported call complaints submitted to the Federal Trade Commission (FTC), complaints collected via crowd-sourced efforts (e.g., 800notes.com), call detail records (CDR) from a large telephony honeypot [1], and honeypot-based phone call audio recordings. Overall, our results show that phone blacklists are capable of blocking a significant fraction of future unwanted calls (e.g., more than 55%). Also, they have a very low false positive rate of only 0.01% for phone numbers of legitimate businesses. We also propose an unsupervised learning method to identify prevalent spam campaigns from different data sources, and show how effective blacklists may be as a defense against such campaigns.

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SoK: Everyone Hates Robocalls: A Survey of Techniques Against Telephone Spam

Cited by 60 publications

References 51 publications

Dial One for Scam: A Large-Scale Analysis of Technical Support Scams

Dial One for Scam: A Large-Scale Analysis of Technical Support Scams

A Machine Learning Approach to Prevent Malicious Calls over Telephony Networks

Towards Measuring the Effectiveness of Telephony Blacklists

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