Kosei Akama (Keio University), Yoshimichi Nakatsuka (ETH Zurich), Masaaki Sato (Tokai University), Keisuke Uehara (Keio University)

Preventing abusive activities caused by adversaries accessing online services at a rate exceeding that expected by websites has become an ever-increasing problem. CAPTCHAs and SMS authentication are widely used to provide a solution by implementing rate limiting, although they are becoming less effective, and some are considered privacy-invasive. In light of this, many studies have proposed better rate-limiting systems that protect the privacy of legitimate users while blocking malicious actors. However, they suffer from one or more shortcomings: (1) assume trust in the underlying hardware and (2) are vulnerable to side-channel attacks.
Motivated by the aforementioned issues, this paper proposes Scrappy: SeCure Rate Assuring Protocol with PrivacY. Scrappy allows clients to generate unforgeable yet unlinkable rate-assuring proofs, which provides the server with cryptographic guarantees that the client is not misbehaving. We design Scrappy using a combination of DAA and hardware security devices. Scrappy is implemented over three types of devices, including one that can immediately be deployed in the real world. Our baseline evaluation shows that the end-to-end latency of Scrappy is minimal, taking only 0.32 seconds, and uses only 679 bytes of bandwidth when transferring necessary data. We also conduct an extensive security evaluation, showing that the rate-limiting capability of Scrappy is unaffected even if the hardware security device is compromised.

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The Impact of Workload on Phishing Susceptibility: An Experiment

Sijie Zhuo (University of Auckland), Robert Biddle (University of Auckland and Carleton University, Ottawa), Lucas Betts, Nalin Asanka Gamagedara Arachchilage, Yun Sing Koh, Danielle Lottridge, Giovanni Russello (University of Auckland)

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Certificate Transparency Revisited: The Public Inspections on Third-party Monitors

Aozhuo Sun (Institute of Information Engineering, Chinese Academy of Sciences), Jingqiang Lin (School of Cyber Science and Technology, University of Science and Technology of China), Wei Wang (Institute of Information Engineering, Chinese Academy of Sciences), Zeyan Liu (The University of Kansas), Bingyu Li (School of Cyber Science and Technology, Beihang University), Shushang Wen (School of…

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LoRDMA: A New Low-Rate DoS Attack in RDMA Networks

Shicheng Wang (Tsinghua University), Menghao Zhang (Beihang University & Infrawaves), Yuying Du (Information Engineering University), Ziteng Chen (Southeast University), Zhiliang Wang (Tsinghua University & Zhongguancun Laboratory), Mingwei Xu (Tsinghua University & Zhongguancun Laboratory), Renjie Xie (Tsinghua University), Jiahai Yang (Tsinghua University & Zhongguancun Laboratory)

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SigmaDiff: Semantics-Aware Deep Graph Matching for Pseudocode Diffing

Lian Gao (University of California Riverside), Yu Qu (University of California Riverside), Sheng Yu (University of California, Riverside & Deepbits Technology Inc.), Yue Duan (Singapore Management University), Heng Yin (University of California, Riverside & Deepbits Technology Inc.)

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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)