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.

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V2X Security: Status and Open Challenges

Jonathan Petit (Director Of Engineering at Qualcomm Technologies) Dr. Jonathan Petit is Director of Engineering at Qualcomm Technologies, Inc., where he leads research in security of connected and automated vehicles (CAV). His team works on designing security solutions, but also develops tools for automotive penetration testing and builds prototypes. His recent work on misbehavior protection…

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Impact Evaluation of Falsified Data Attacks on Connected Vehicle...

Shihong Huang (University of Michigan, Ann Arbor), Yiheng Feng (Purdue University), Wai Wong (University of Michigan, Ann Arbor), Qi Alfred Chen (UC Irvine), Z. Morley Mao and Henry X. Liu (University of Michigan, Ann Arbor) Best Paper Award Runner-up ($200 cash prize)!

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PFirewall: Semantics-Aware Customizable Data Flow Control for Smart Home...

Haotian Chi (Temple University), Qiang Zeng (University of South Carolina), Xiaojiang Du (Temple University), Lannan Luo (University of South Carolina)

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HERA: Hotpatching of Embedded Real-time Applications

Christian Niesler (University of Duisburg-Essen), Sebastian Surminski (University of Duisburg-Essen), Lucas Davi (University of Duisburg-Essen)

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