CANDID

Enhancing Vehicle Cyber Security with Anomaly Detection

Anomaly detection for CAN systems

Charles River Analytics is developing a software/hardware solution to detect and mitigate cyber attacks on CANs using advanced AI/ML techniques. These techniques learn what normal CAN traffic and electronic control unit (ECU) behavior looks like so they can identify anomalies as soon as
they occur.

When CANDID identifies an intrusion, it can handle the attack without driver input, dropping or modifying corrupt or malicious system messages without impacting vehicle operation.

Cyber security challenges facing intelligent vehicles

Vehicles are increasingly connected with their environment and other vehicles on the road using wireless communication tech, like Bluetooth and Wi-Fi. This greater connectivity exposes a broader attack surface. Regardless of make or model, vehicles rely on the same electronic components, made by the same manufacturers­—making cyber vulnerabilities widespread and quick to propagate. We recognize that cybersecurity and vehicle protection are primary as the auto industry trends toward autonomous driving.

One particular vulnerability is the Controller Area Network (CAN). CANs have little built-in security, yet handle communications for the ECUs that are critical to the vehicle. ECUs run most essential vehicle functions, including brakes, engine, fuel injection, and tire pressure. Access to these functions makes cyber attacks on CANs and ECUs extremely dangerous, potentially resulting in a breach of confidential information or even total loss of vehicle control.

Key Advantages

CANDID equips your vehicles with state-of-the-art cyber attack detection and mitigation tech to keep your drivers and their data safe.

High cyber attack detection rates, including zero-day attacks

No modifications to existing protocols, systems, or components required

High cyber attack detection rates, including zero-day attacks

No modifications to existing protocols, systems, or components required

This material is based upon work supported by the Army Contracting Command – Warren under Contract No. W56HZV-20-C-0092. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Army Contracting Command – Warren.

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