Sayak Saha Roy, Unique Karanjit, Shirin Nilizadeh (The University of Texas at Arlington)

Twitter maintains a blackbox approach for detecting malicious URLs shared on its platform. In this study, we evaluate the efficiency of their detection mechanism against newer phishing and drive-by download threats posted on the website over three different time periods of the year. Our findings indicate that several threats remained undetected by Twitter, with the majority of them originating from nine different free website hosting services. These URLs targeted 19 popular organizations and also distributed malicious files from 9 different threat categories. Moreover, the malicious websites hosted under these services were also less likely to get detected by URL scanning tools than other similar threats hosted elsewhere, and were accessible on their respective domains for a much longer duration. We believe that the aforementioned features, combined with the ease of access (drag and drop website creating interface, up-to-date SSL certification, reputed domain, etc.) provides attackers a fast and convenient way to create malicious attacks using these services. On the other hand, we also observed that the majority of the URLs which were actually detected by Twitter remained active on the platform throughout our study, allowing them to be easily distributed across the platform. Also, several benign websites in our dataset were detected by Twitter as being malicious. We hypothesize that this is caused due to a blocklisting procedure used by Twitter, which detects all URLs originating from certain domains, irrespective of their content. Thus, our results identify a family of potent threats, which are distributed freely on Twitter, and are also not detected by the majority of URL scanning tools, or even the services which host them, thus making the need for a more thorough URL blocking approach from Twitter’s end more apparent.

View More Papers

Location Data and COVID-19 Contact Tracing: How Data Privacy...

Callie Monroe, Faiza Tazi, Sanchari Das (university of Denver)

Read More

Effects of Precise and Imprecise Value-Set Analysis (VSA) Information...

Laura Matzen, Michelle A Leger, Geoffrey Reedy (Sandia National Laboratories)

Read More

The Bluetooth CYBORG: Analysis of the Full Human-Machine Passkey...

Michael Troncoso (Naval Postgraduate School), Britta Hale (Naval Postgraduate School)

Read More

Favocado: Fuzzing the Binding Code of JavaScript Engines Using...

Sung Ta Dinh (Arizona State University), Haehyun Cho (Arizona State University), Kyle Martin (North Carolina State University), Adam Oest (PayPal, Inc.), Kyle Zeng (Arizona State University), Alexandros Kapravelos (North Carolina State University), Gail-Joon Ahn (Arizona State University and Samsung Research), Tiffany Bao (Arizona State University), Ruoyu Wang (Arizona State University), Adam Doupe (Arizona State University),…

Read More