Michael Clark (Brigham Young University), Scott Ruoti (The University of Tennessee), Michael Mendoza (Imperial College London), Kent Seamons (Brigham Young University)

Users struggle to select strong passwords. System-assigned passwords address this problem, but they can be difficult for users to memorize. While password managers can help store system-assigned passwords, there will always be passwords that a user needs to memorize, such as their password manager’s master password. As such, there is a critical need for research into helping users memorize system-assigned passwords. In this work, we compare three different designs for password memorization aids inspired by the method of loci or memory palace. Design One displays a two-dimensional scene with objects placed inside it in arbitrary (and randomized) positions, with Design Two fixing the objects’ position within the scene, and Design Three displays the scene using a navigable, three-dimensional representation. In an A-B study of these designs, we find that, surprisingly, there is no statistically significant difference between the memorability of these three designs, nor that of assigning users a passphrase to memorize, which we used as the control in this study. However, we find that when perfect recall failed, our designs helped users remember a greater portion of the encoded system-assigned password than did a passphrase, a property we refer to as durability. Our results indicate that there could be room for memorization aids that incorporate fuzzy or error-correcting authentication. Similarly, our results suggest that simple (i.e., cheap to develop) designs of this nature may be just as effective as more complicated, high-fidelity (i.e., expensive to develop) designs.

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DeepGo: Predictive Directed Greybox Fuzzing

Peihong Lin (National University of Defense Technology), Pengfei Wang (National University of Defense Technology), Xu Zhou (National University of Defense Technology), Wei Xie (National University of Defense Technology), Gen Zhang (National University of Defense Technology), Kai Lu (National University of Defense Technology)

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Stacking up the LLM Risks: Applied Machine Learning Security

Dr. Gary McGraw, Berryville Institute of Machine Learning

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Facilitating Non-Intrusive In-Vivo Firmware Testing with Stateless Instrumentation

Jiameng Shi (University of Georgia), Wenqiang Li (Independent Researcher), Wenwen Wang (University of Georgia), Le Guan (University of Georgia)

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