"New Graph-Based Statistical Method Detects Threats To Vehicular Communications Networks"
Researchers at the University of Maryland, Baltimore County (UMBC) and the University of Michigan-Dearborn worked together to develop a technique for detecting breaches in the security of vehicular communications networks. The Controller Area Network (CAN) is the most popular intra-vehicular communications network in the automobile industry as it is simple to use. However, the simplicity of this network that makes it appealing for consumers and manufacturers increases the risk of security incidents. Using the CAN, it is possible to remotely control a vehicle from other devices, making it both a feature and a major security concern. A malicious actor can take over the network and send new commands to the vehicle that could disable brakes or cause engine failure, posing a significant threat to consumers' safety. The method developed by the researchers to eradicate these possible threats involves the creation of graph-based anomaly detection techniques. This article continues to discuss the new graph-based statistical method designed to detect intruders or threats to vehicular communications networks and the importance of addressing the vulnerabilities associated with these networks.