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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Yi Zhu (State University of New York at Buffalo), Chenglin Miao (University of Georgia), Foad Hajiaghajani (State University of New York at Buffalo), Mengdi Huai (University of Virginia), Lu Su (Purdue University) and Chunming Qiao (State University of New York at Buffalo)

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Detecting Obfuscated Function Clones in Binaries using Machine Learning

Michael Pucher (University of Vienna), Christian Kudera (SBA Research), Georg Merzdovnik (SBA Research)

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FakeGuard: Exploring Haptic Response to Mitigate the Vulnerability in...

Aditya Singh Rathore (University at Buffalo, SUNY), Yijie Shen (Zhejiang University), Chenhan Xu (University at Buffalo, SUNY), Jacob Snyderman (University at Buffalo, SUNY), Jinsong Han (Zhejiang University), Fan Zhang (Zhejiang University), Zhengxiong Li (University of Colorado Denver), Feng Lin (Zhejiang University), Wenyao Xu (University at Buffalo, SUNY), Kui Ren (Zhejiang University)

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