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DeepPOSE: Securing Transportation systems from GPS Spoofing Attacks

Coastal Virginia Node

Principal Investigator: 
Chunsheng Xin, professor in the Center for Cybersecurity Education and Research, Old Dominion University

Co-Principal Investigators: 
Yanxiao Zhao, associate professor, electrical and computer engineering, Virginia Commonwealth University; Kevin Kefauver, technical director, Global Center for Automotive Performance Simulation; Danella Zhao, associate professor, computer science, ODU

Project Description: 
Today the Global Positioning System (GPS) service is widely used in our daily lives. Smartphones, wearable devices, aircrafts, unmanned aerial vehicles (UAVs), self-driving cars or autonomous vehicles all rely on GPS to benefit from location based services, e.g., navigation, truck/vehicle monitoring, reporting self location in emergency or for rescue, searching nearby gas stations, restaurants, hotels, etc. While GPS becomes an indispensable element in our daily lives, it is rather vulnerable to GPS spoofing attacks even by low-cost hardware, resulting in potentially life-threatening impact. While there has been a long history of studying GPS spoofing, previous solutions to detect GPS spoofing were either applicable to limited scenarios only, or not effective to smart attackers. Furthermore, new challenges, such as varying attack surfaces and lack of mitigation techniques, make the problem even more challenging today. Capitalizing on a strong academia-industry partnership, a team of faculty from ODU and VCU and engineers from GCAPS VTT LLC proposes a holistic framework DeepPOSE to utilize multimodal sensor data to detect and mitigate GPS spoofing attacks to transportation systems, using Deep Learning technologies and emerging wireless communication techniques. The developed system will be prototyped and integrated into the product of GCAPS, a leading vehicle simulation framework, and results in potentially large economic impact on the Commonwealth of Virginia.