SafeRide’s CAN Optimizer uses advanced machine learning algorithms to compress raw CAN bus data, reducing the bandwidth required to upload information to the cloud. This is not a trivial thing: While uploading raw CAN data to the cloud enables advanced anomaly detection capabilities, the process consumes a significant amount of bandwidth. SafeRide’s CAN Optimizer dramatically decreases the bandwidth needed to do so by providing 98-99% reduction in data size, with a typical lossless compression ratio more than 15 times better than other compression algorithms that are currently on the market. This will greatly benefit OEMs and fleet managers by further helping to uncover unknown cybersecurity vulnerabilities, identifying malfunctions before they happen, and even detecting misuse and abuse of vehicles.
The company’s machine learning model has proven accurate enough that the car only needs to send data to the cloud when it does not follow the most probable prediction, explained SafeRide CEO Yossi Vardi. “Combined with our vSentry multi-layer cybersecurity solution, CAN Optimizer allows for a new level of zero-day vulnerability detection capabilities, as well as fleet operation efficiency optimization,” he explained. These advancements lead to drastic cost reduction in both data and storage for OEMs and fleet operators, the company promises.
SafeRide will showcase its vSentry and CAN Optimizer solution with industry-leading partner, Irdeto, at the Paris Motor Show on October 1-6, on booth J60, Pavillion 7.