For their senior capstone project, Chirag Sharma, Oscar Jaramillo, Borhan Fanayan, Ahmed Amkor, Ryan Davidson, and Abudullah Alkhudair developed software that can identify malicious cyberattacks on a vehicle’s internal networks. Photo by: Ron Aira
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Putting the Brakes on Automobile Cyberattacks It sounds like a scene from a James Bond movie: A villain hacks 007’s car to take control and crash it. Something like this could happen if cybersecurity in automobiles isn’t beefed up. Cars today operate a lot like computers on wheels, and several years ago, researchers showed they could hijack a moving vehicle, says Ryan Davidson, BS Electrical Engineering ’17. “The problem is not common now, but it will become more of an issue in the future.” So, for their senior capstone project, Davidson and fve other Mason Engineering students developed software that can identify malicious cyberattacks on a vehicle’s internal networks. The backstory: Embedded devices, called electronic control units, regulate nearly every function of an automobile, including the brakes, steering, and acceleration. They send information to other components through the controller area network. Cyber attackers could take over a driver’s steering or braking capability by hacking into one of the devices on the network, says the group’s faculty 36 | MASON ENGINEERING ANNUAL REPORT 2018
supervisor Kai Zeng, an associate professor in the Department of Electrical and Computer Engineering. The students’ goal was to create a system to determine if the messages to and from the devices were legitimate and not from hackers. To do this, they applied machine-learning algorithms to develop a “fngerprint” for each legitimate message on the network, and they put those fngerprints into a database so they could differentiate between trusted and malicious messages on the network. “The goal was to detect the bad guys without rejecting good signals,” Zeng says. After they created the software, they tested it by sending both real and fake signals. They ran this process on several vehicles including a Toyota Camry, Chevy, and BMW. The intrusion detection system was able to identify the signals with up to 90 percent accuracy, Davidson says. The students didn’t modify any device on the car’s current network, he says. “That was probably the most important part of this project. People have been able to do some-
