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

coucouArray ( [post_type] => ndss-paper [post_status] => publish [posts_per_page] => 4 [orderby] => rand [tax_query] => Array ( [0] => Array ( [taxonomy] => category [field] => id [terms] => Array ( [0] => 40 [1] => 47 ) ) ) [post__not_in] => Array ( [0] => 7311 ) )

Work-in-Progress: A Large-Scale Long-term Analysis of Online Fraud across...

Yi Han, Shujiang Wu, Mengmeng Li, Zixi Wang, and Pengfei Sun (F5)

Read More

Demo #1: Curricular Reinforcement Learning for Robust Policy in...

Yunzhe Tian, Yike Li, Yingxiao Xiang, Wenjia Niu, Endong Tong, and Jiqiang Liu (Beijing Jiaotong University)

Read More

Tag of the Dead: How Terminated SaaS Tags Become...

Takahito Sakamoto, Takuya Murozono (DataSign Inc)

Read More

Shepherd: A Generic Approach to Automating Website Login

H. Jonker, S. Karsch, B. Krumnow, M. Sleegers

Read More

Privacy Starts with UI: Privacy Patterns and Designer Perspectives in UI/UX Practice

Anxhela Maloku (Technical University of Munich), Alexandra Klymenko (Technical University of Munich), Stephen Meisenbacher (Technical University of Munich), Florian Matthes (Technical University of Munich)

Vision: Profiling Human Attackers: Personality and Behavioral Patterns in Deceptive Multi-Stage CTF Challenges

Khalid Alasiri (School of Computing and Augmented Intelligence Arizona State University), Rakibul Hasan (School of Computing and Augmented Intelligence Arizona State University)

From Underground to Mainstream Marketplaces: Measuring AI-Enabled NSFW Deepfakes on Fiverr

Mohamed Moustafa Dawoud (University of California, Santa Cruz), Alejandro Cuevas (Princeton University), Ram Sundara Raman (University of California, Santa Cruz)