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1. "So-called privacy breeds evil": Narrative Justifications for Intimate Partner Surveillance in Online Forums

   https://doi.org/10.1145/3432909  

   Bellini, Rosanna; Tseng, Emily; McDonald, Nora; Greenstadt, Rachel; McCoy, Damon; Ristenpart, Thomas; Dell, Nicola (January 2021, Proceedings of the ACM on Human-Computer Interaction)

   null (Ed.)

   A growing body of research suggests that intimate partner abusers use digital technologies to surveil their partners, including by installing spyware apps, compromising devices and online accounts, and employing social engineering tactics. However, to date, this form of privacy violation, called intimate partner surveillance (IPS), has primarily been studied from the perspective of victim-survivors. We present a qualitative study of how potential perpetrators of IPS harness the emotive power of sharing personal narratives to validate and legitimise their abusive behaviours. We analysed 556 stories of IPS posted on publicly accessible online forums dedicated to the discussion of sexual infidelity. We found that many users share narrative posts describing IPS as they boast about their actions, advise others on how to perform IPS without detection, and seek suggestions for next steps to take. We identify a set of common thematic story structures, justifications for abuse, and outcomes within the stories that provide a window into how these individuals believe their behaviour to be justified. Using these stories, we develop a four-stage framework that captures the change in a potential perpetrator's approach to IPS. We use our findings and framework to guide a discussion of efforts to combat abuse, including how we can identify crucial moments where interventions might be safely applied to prevent or deescalate IPS. 

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2. SoK: Hate, Harassment, and the Changing Landscape of Online Abuse

   Thomas, Kurt; Akhawe, Devdatta; Bailey, Michael; Boneh, Dan; Bursztein, Elie; Consolvo, Sunny; Dell, Nicola; Durumeric, Zakir; Kelley, Patrick; Kumar, Deepak; et al (May 2021, 42nd IEEE Symposium on Security and Privacy)

   null (Ed.)

   We argue that existing security, privacy, and anti-abuse protections fail to address the growing threat of online hate and harassment. In order for our community to understand and address this gap, we propose a taxonomy for reasoning about online hate and harassment. Our taxonomy draws on over 150 interdisciplinary research papers that cover disparate threats ranging from intimate partner violence to coordinated mobs. In the process, we identify seven classes of attacks—such as toxic content and surveillance—that each stem from different attacker capabilities and intents. We also provide longitudinal evidence from a three-year survey that hate and harassment is a pervasive, growing experience for online users, particularly for at-risk communities like young adults and people who identify as LGBTQ+. Responding to each class of hate and harassment requires a unique strategy and we highlight five such potential research directions that ultimately empower individuals, communities, and platforms to do so. 

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3. Shoulder Surfing on Mobile Authentication: Perception vis-a-vis Performance from the Attacker's Perspective

   https://doi.org/10.1109/ISI58743.2023.10297219  

   Wang, Kanlun; Zhou, Lina; Zhang, Dongsong; Lai, Jianwei (October 2023, IEEE)

   Shoulder-surfing studies in the context of mobile user authentication have focused on evaluating the attackers' performance, yet have paid much less attention to their perception of the shoulder-surfing process. Whether and how the shoulder-surfing setting might affect the attackers' perception remains under-explored. This study aims to investigate the perception of shoulder surfers with two different password-based mobile user authentication methods and three different observation angles. Moreover, this work examines the relationship between the attackers' perception and performance in shoulder surfing and the possible moderating effect of the authentication method for the first time. Based on the data collected from an online experiment, our analysis results reveal the effects of authentication methods and observation angles on the attackers' perception in terms of cognitive workload, observation clarity, and repetitive learning advantage. In addition, the results also show that the relationship between the attackers' cognitive workload and performance in shoulder surfing varies with the mobile user authentication method. Our findings not only deepen the understanding of shoulder-surfing attacks from an attacker's perspective, but also facilitate developing countermeasures for shoulder-surfing attacks. 

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4. Denial-of-service attack detection using machine learning in network-on-chip architectures

   https://doi.org/10.1145/3479876.3481589  

   Sudusinghe, Chamika; Charles, Subodha; Mishra, Prabhat (October 2021, IEEE/ACM International Symposium on Networks-on-Chip (NOCS))

   State-of-the-art System-on-Chip (SoC) designs consist of many Intellectual Property (IP) cores that interact using a Network-on-Chip (NoC) architecture. SoC designers increasingly rely on global supply chains for obtaining third-party IPs. In addition to inherent vulnerabilities associated with utilizing third-party IPs, NoC based SoCs enable attackers to exploit the distributed nature of NoC and its connectivity with various IPs to launch a plethora of attacks. Specifically, Denial-of-Service (DoS) attacks pose a serious threat in degrading the SoC performance by flooding the NoC with unnecessary packets. In this paper, we present a machine learning-based runtime monitoring mechanism to detect DoS attacks. The models are statically trained and used for runtime attack detection leading to minimum runtime performance overhead. Our approach is capable of detecting DoS attacks with high accuracy, even in the presence of unpredictable NoC traffic patterns caused by various application mappings. We extensively explore machine learning models and features to provide a comprehensive study on how to use machine learning for DoS attack detection in NoC-based SoCs. 

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5. Beyond Bot Detection: Combating Fraudulent Online Survey Takers

   https://doi.org/10.1145/3485447.3512230  

   Zhang, Ziyi; Zhu, Shuofei; Mink, Jaron; Xiong, Aiping; Song, Linhai; Wang, Gang (April 2022, Proceedings of the Web Conference 2022)

   Different techniques have been recommended to detect fraudulent responses in online surveys, but little research has been taken to systematically test the extent to which they actually work in practice. In this paper, we conduct an empirical evaluation of 22 antifraud tests in two complementary online surveys. The first survey recruits Rust programmers on public online forums and social media networks. We find that fraudulent respondents involve both bot and human characteristics. Among different anti-fraud tests, those designed based on domain knowledge are the most effective. By combining individual tests, we can achieve a detection performance as good as commercial techniques while making the results more explainable. To explore these tests under a broader context, we ran a different survey on Amazon Mechanical Turk (MTurk). The results show that for a generic survey without requiring users to have any domain knowledge, it is more difficult to distinguish fraudulent responses. However, a subset of tests still remain effective. 

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