Harshad Sathaye (Northeastern University), Gerald LaMountain (Northeastern University), Pau Closas (Northeastern University), Aanjhan Ranganathan (Northeastern University)

It is well-known that GPS is vulnerable to signal spoofing attacks. Although several spoofing detection techniques exist, they are incapable of mitigation and recovery from stealthy attackers. In this work, we present SemperFi, a single antenna GPS receiver capable of tracking legitimate GPS satellite signals and estimating the true location even against strong adversaries. Our design leverages a combination of the Extended Kalman Filter based GPS failsafe mechanism built into majority of UAVs and a custom designed legitimate signal retriever module to detect and autonomously recover from majority of spoofing attacks. We develop algorithms to carefully synthesize recovery signals and extend the successive interference cancellation technique to preserve the legitimate signal’s ToA, while eliminating the attacker’s signal. For strong adversaries capable of stealthy and seamless takeover, SemperFi uses brief maneuvers designed to exploit the short-term stability of inertial sensors and identify stealthy spoofing attacks. We implement SemperFi in GNSS-SDR, an open-source software-defined GNSS receiver, and evaluate its performance using UAV simulators, real drones, a variety of real-world GPS datasets, as well as on various embedded platforms. Our evaluation results indicate that in many scenarios, SemperFi can identify adversarial peaks by executing flight patterns less than 100 m long and recover the true location within 0.54 s (Jetson Xavier). We show that our receiver is secure against both naive and stealthy spoofers who exploit inertial sensor errors and execute seamless takeover attacks. Furthermore, we design SemperFi as a pluggable module capable of generating a spoofer-free GPS signal for processing on any commercial-off-the-shelf GPS receiver available today. Finally, we release our implementation to the community for usage and further research.

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Interpretable Federated Transformer Log Learning for Cloud Threat Forensics

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VPNInspector: Systematic Investigation of the VPN Ecosystem

Reethika Ramesh (University of Michigan), Leonid Evdokimov (Independent), Diwen Xue (University of Michigan), Roya Ensafi (University of Michigan)

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FirmWire: Transparent Dynamic Analysis for Cellular Baseband Firmware

Grant Hernandez (University of Florida), Marius Muench (Vrije Universiteit Amsterdam), Dominik Maier (TU Berlin), Alyssa Milburn (Vrije Universiteit Amsterdam), Shinjo Park (TU Berlin), Tobias Scharnowski (Ruhr-University Bochum), Tyler Tucker (University of Florida), Patrick Traynor (University of Florida), Kevin Butler (University of Florida)

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FedCRI: Federated Mobile Cyber-Risk Intelligence

Hossein Fereidooni (Technical University of Darmstadt), Alexandra Dmitrienko (University of Wuerzburg), Phillip Rieger (Technical University of Darmstadt), Markus Miettinen (Technical University of Darmstadt), Ahmad-Reza Sadeghi (Technical University of Darmstadt), Felix Madlener (KOBIL)

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