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Abstract:
We''ll discuss anomaly detection on vehicle CAN BUS. We developed a novel solution for neural networks to detect anomalies in CAN data. Due to the inherent characteristics of controller area (CAN) networks, such as lack of authentication and following a broadcast routing scheme, devices connected to a CAN network are exposed to a broad range of cyberattacks. Our work aims to alleviate this problem by providing an anomaly detection mechanism, that is, identifying deviations from normal network traffic, to enhance the security of CAN networks. This invention is leveraged as one of the intrusion detection methods in a broader NVIDIA embedded software security system deployed in automotive platforms. In this specific application, the embedded system is a car computer -- an embedded system deployed in modern vehicles. Typical examples: infotainment systems, ADAS units, dashboards, head units. The vulnerable endpoints are all the peripherals connected to the computer. Typical examples: sensors, cameras, media devices, local and wide area communication interfaces and devices (for example, WiFi, BT, cellular), specific car network interfaces and devices.
We''ll discuss anomaly detection on vehicle CAN BUS. We developed a novel solution for neural networks to detect anomalies in CAN data. Due to the inherent characteristics of controller area (CAN) networks, such as lack of authentication and following a broadcast routing scheme, devices connected to a CAN network are exposed to a broad range of cyberattacks. Our work aims to alleviate this problem by providing an anomaly detection mechanism, that is, identifying deviations from normal network traffic, to enhance the security of CAN networks. This invention is leveraged as one of the intrusion detection methods in a broader NVIDIA embedded software security system deployed in automotive platforms. In this specific application, the embedded system is a car computer -- an embedded system deployed in modern vehicles. Typical examples: infotainment systems, ADAS units, dashboards, head units. The vulnerable endpoints are all the peripherals connected to the computer. Typical examples: sensors, cameras, media devices, local and wide area communication interfaces and devices (for example, WiFi, BT, cellular), specific car network interfaces and devices.  Back
 
Topics:
AI Application, Deployment & Inference, Deep Learning & AI Frameworks, Cyber Security, Autonomous Vehicles
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8347
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