Scientists have created an artificial intelligence tool to improve the privacy and security of vehicles in the Internet of Vehicles (IoV) ecosystem.
The IoV connects vehicles to each other and to infrastructure like parking lots, road systems, and pedestrians. However, this network faces serious cyber threats because of limited onboard resources and vulnerable sensors in vehicles. These issues make vehicles an easy target for attackers.
To solve this, researchers from the University of Sharjah, the University of Maryland, and Abdul Wali Khan University have designed a lightweight and reliable machine-learning (ML) authentication system. This system helps vehicles communicate securely without depending too much on cloud servers, which can cause delays and bandwidth issues.
The tool uses a list of masked identities (MaskIDs) and secret keys, shared by a trusted authority, to verify vehicles and edge servers. By processing data locally on edge servers, the system ensures faster responses and reduces the risk of cyberattacks like impersonation or interception.
In testing, this ML-based solution showed strong performance, with better communication, processing, and storage efficiency compared to existing methods. It also defends against common cyber threats, making it a promising advancement for IoV security.
This tool could be a game-changer in protecting vehicles and drivers in the connected world of transportation.
Source: forensicmag
