Mohit Kumar Jangid (The Ohio State University), Yue Zhang (Computer Science & Engineering, Ohio State University), Zhiqiang Lin (The Ohio State University)

Bluetooth is a leading wireless communication technology used by billions of Internet of Things (IoT) devices today. Its ubiquity demands systematic security scrutiny. A key ingredient in Bluetooth security is secure pairing, which includes Numeric comparison (NC) and Passkey Entry (PE). However, most prior formal efforts have considered only NC, and PE has not yet been formally studied in depth. In this paper, we propose a detailed formal analysis of the PE protocol. In particular, we present a generic formal model, built using Tamarin, to verify the security of PE by precisely capturing the protocol behaviors and attacker capabilities. Encouragingly, it rediscovers three known attacks (confusion attacks, static passcode attacks, and reflection attacks), and more importantly, also uncovers two new attacks (group guessing attacks and ghost attacks) spanning across diverse attack vectors (e.g., static variable reuse, multi-threading, reflection, human error, and compromise device). Finally, after applying fixes to each vulnerability, our model further proves the confidentiality and authentication properties of the PE protocol using an inductive base model.

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Breaking and Fixing Virtual Channels: Domino Attack and Donner

Lukas Aumayr (TU Wien), Pedro Moreno-Sanchez (IMDEA Software Institute), Aniket Kate (Purdue University / Supra), Matteo Maffei (Christian Doppler Laboratory Blockchain Technologies for the Internet of Things / TU Wien)

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ReScan: A Middleware Framework for Realistic and Robust Black-box...

Kostas Drakonakis (FORTH), Sotiris Ioannidis (Technical University of Crete), Jason Polakis (University of Illinois at Chicago)

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Post-GDPR Threat Hunting on Android Phones: Dissecting OS-level Safeguards...

Mark Huasong Meng (National University of Singapore), Qing Zhang (ByteDance), Guangshuai Xia (ByteDance), Yuwei Zheng (ByteDance), Yanjun Zhang (The University of Queensland), Guangdong Bai (The University of Queensland), Zhi Liu (ByteDance), Sin G. Teo (Agency for Science, Technology and Research), Jin Song Dong (National University of Singapore)

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Improving In-vehicle Networks Intrusion Detection Using On-Device Transfer Learning

Sampath Rajapaksha (Robert Gordon University), Harsha Kalutarage (Robert Gordon University), M.Omar Al-Kadri (Birmingham City University), Andrei Petrovski (Robert Gordon University), Garikayi Madzudzo (Horiba Mira Ltd)

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