Muslum Ozgur Ozmen, Habiba Farrukh, Hyungsub Kim, Antonio Bianchi, Z. Berkay Celik (Purdue University)

Drone swarms are becoming increasingly prevalent in important missions, including military operations, rescue tasks, environmental monitoring, and disaster recovery. Member drones coordinate with each other to efficiently and effectively accomplish a given mission. To automatically coordinate a swarm, member drones exchange critical messages (e.g., their positions, locations of identified obstacles, and detected search targets) about their observed environment and missions over wireless communication channels. Therefore, swarms need a pairing system to establish secure communication channels that protect the confidentiality and integrity of the messages. However, swarm properties and the open physical environment in which they operate bring unique challenges in establishing cryptographic keys between drones.

In this paper, we first outline an adversarial model and the ideal design requirements for secure pairing in drone swarms. We then survey existing human-in-the-loop-based, context-based, and public key cryptography (PKC) based pairing methods to explore their feasibility in drone swarms. Our exploration, unfortunately, shows that existing techniques fail to fully meet the unique requirements of drone swarms. Thus, we propose research directions that can meet these requirements for secure, energy-efficient, and scalable swarm pairing systems.

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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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Lightning Community Shout-Outs to:

(1) Jonathan Petit, Secure ML Performance Benchmark (Qualcomm) (2) David Balenson, The Road to Future Automotive Research Datasets: PIVOT Project and Community Workshop (USC Information Sciences Institute) (3) Jeremy Daily, CyberX Challenge Events (Colorado State University) (4) Mert D. Pesé, DETROIT: Data Collection, Translation and Sharing for Rapid Vehicular App Development (Clemson University) (5) Ning…

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Folk Models of Misinformation on Social Media

Filipo Sharevski (DePaul University), Amy Devine (DePaul University), Emma Pieroni (DePaul University), Peter Jachim (DePaul University)

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