Muhammad Hassan, Mahnoor Jameel, Masooda Bashir (University of Illinois at Urbana Champaign)

Social network platforms are now widely used as a mode of communication globally due to their popularity and their ease of use. Among the various content-sharing capabilities made available via these applications, link-sharing is a common activity among social media users. While this feature provides a desired functionality for the platform users, link sharing enables attackers to exploit vulnerabilities and compromise users’ devices. Attackers can exploit this content-sharing feature by posting malicious/harmful URLs or deceptive posts and messages which are intended to hide a dangerous link. However, it is not clear how the most common social media applications monitor and/or filter when their users share malicious URLs or links through their platforms. To investigate this security vulnerability, we designed an exploratory study to examine the top five android social media applications’ performance when it comes to malicious link sharing. The aim was to determine if the selected applications had any filtering or defenses against malicious URL sharing. Our results show that most of the selected social media applications did not have an effective defense against the posting and spreading of malicious URLs. While our results are exploratory, we believe our study demonstrates the presence of a vital security vulnerability that malicious attackers or unaware users can use to spread harmful links. In addition, our findings can be used to improve our understanding of link-based attacks as well as the design of security measures that usability into account

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Folk Models of Misinformation on Social Media

Filipo Sharevski (DePaul University), Amy Devine (DePaul University), Emma Pieroni (DePaul University), Peter Jachim (DePaul University)

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Why People Still Fall for Phishing Emails: An Empirical...

Asangi Jayatilaka (Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, School of Computing Technologies, RMIT University), Nalin Asanka Gamagedara Arachchilage (School of Computer Science, The University of Auckland), M. Ali Babar (Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide)

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RAI2: Responsible Identity Audit Governing the Artificial Intelligence

Tian Dong (Shanghai Jiao Tong University), Shaofeng Li (Shanghai Jiao Tong University), Guoxing Chen (Shanghai Jiao Tong University), Minhui Xue (CSIRO's Data61), Haojin Zhu (Shanghai Jiao Tong University), Zhen Liu (Shanghai Jiao Tong University)

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OptRand: Optimistically Responsive Reconfigurable Distributed Randomness

Adithya Bhat (Purdue University), Nibesh Shrestha (Rochester Institute of Technology), Aniket Kate (Purdue University), Kartik Nayak (Duke University)

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