Jens Christian Dalgaard, Niek A. Janssen, Oksana Kulyuk, Carsten Schurmann (IT University of Copenhagen)

Cybersecurity concerns are increasingly growing across different sectors globally, yet security education remains a challenge. As such, many of the current proposals suffer from drawbacks, such as failing to engage users or to provide them with actionable guidelines on how to protect their security assets in practice. In this work, we propose an approach for designing security trainings from an adversarial perspective, where the audience learns about the specific methodology of the specific methods, which attackers can use to break into IT systems. We design a platform based on our proposed approach and evaluate it in an empirical study (N = 34), showing promising results in terms of motivating users to follow security policies.

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Evaluating LLMs Towards Automated Assessment of Privacy Policy Understandability

Keika Mori (Deloitte Tohmatsu Cyber LLC, Waseda University), Daiki Ito (Deloitte Tohmatsu Cyber LLC), Takumi Fukunaga (Deloitte Tohmatsu Cyber LLC), Takuya Watanabe (Deloitte Tohmatsu Cyber LLC), Yuta Takata (Deloitte Tohmatsu Cyber LLC), Masaki Kamizono (Deloitte Tohmatsu Cyber LLC), Tatsuya Mori (Waseda University, NICT, RIKEN AIP)

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A Robust Counting Sketch for Data Plane Intrusion Detection

Sian Kim (Ewha Womans University), Changhun Jung (Ewha Womans University), RhongHo Jang (Wayne State University), David Mohaisen (University of Central Florida), DaeHun Nyang (Ewha Womans University)

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podft: On Accelerating Dynamic Taint Analysis with Precise Path...

Zhiyou Tian (Xidian University), Cong Sun (Xidian University), Dongrui Zeng (Palo Alto Networks), Gang Tan (Pennsylvania State University)

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BinaryInferno: A Semantic-Driven Approach to Field Inference for Binary...

Jared Chandler (Tufts University), Adam Wick (Fastly), Kathleen Fisher (DARPA)

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