“New ‘Attacker’ Device May Help Enhance Safety in Autonomous Cars: Experts Weigh In”

Researchers Develop ‘Attacker’ Device to Enhance Autonomous Car Safety

A team of researchers at the University of California – San Diego and Northeastern University have developed a new type of algorithm that can mimic an attacking device to identify vulnerabilities in autonomous vehicle security. The report entitled “mmSpoof: Resilient Spoofing of Automotive millimeter-wave Radars using a Reflect Array” discusses an algorithm that will help researchers identify areas for improvement in autonomous driving security.

The expansion of autonomous vehicles has brought the issue of safety and security to the forefront. Today’s self-driving vehicles rely on millimeter-wave (mmWave) radio frequencies for self-driving and driver-assisted features. Each vehicle sends out radio waves that are then reflected by surrounding objects. The reflected waves detect obstacles through changes in their frequency and by measuring how long it takes for the waves to return to the vehicle. However, just like any wireless system, there is a risk of cyberattacks.

Understanding Spoofing in Autonomous Cars

Attackers can interfere with a self-driving car’s return signal through a cyber attack known as spoofing. The attacker sends out another radio signal addressing the vehicle’s radio wave frequency. The returning signal is then manipulated by the attacker to make the self-driving car believe that there is an obstacle in its path. The vehicle may then brake suddenly, increasing the risk of an accident. This has highlighted the need for a system that can detect and prevent cyberattacks.

The Algorithm That Mimics an Attacking Device

Previous efforts to develop an attacking device to test a car’s resistance to such manipulation of radar or millimeter-wave technology used the victim’s radio system against it. The algorithm in the mmSpoof reflects array can circumvent such methods and add to the cybersecurity framework for autonomous vehicles.

The reflection technique relies on the ability of the attacker to manipulate the reflected signal at the speed of light. It ensures that the returning signal is difficult to separate from the original, making it hard for the vehicle to differentiate between the real obstacle and the attacker’s reflected signal. The research goes further to explain that such a technique “requires no prior knowledge of the victim radar to spoof arbitrary distance and velocity values.”

Advice on Improving Vehicle Security

The team suggests various countermeasures to prevent these types of attacks. It suggests using a high-resolution radar or cameras and “light detecting and ranging” (LiDAR) to accurately identify the correct reflection. Additionally, authorities can add localised security knowledge to genuinely check and verify that the right people were authorising action.

These are steps that authorities need to adopt to ensure secure and safe autonomous vehicles. The vehicle industry relies on the safety and security of its customers, and any vulnerability in the autonomous system poses an immense risk. Consequently, embracing new ‘attacker’ devices that help identify weak spots in the self-driving system will inevitably lead to better security measures and reduce the risk of cyberattacks on autonomous vehicles.