Telematic Intrusion Detection for Connected Vehicle CAN Bus Security
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Solution Overview
Problem
Modern vehicles are vulnerable to cyberattacks due to the lack of strong security measures in the original CAN bus architecture, which exposes them to unauthorized access, data integrity threats, and privacy risks.
Innovation Solution
A comprehensive cybersecurity system that includes a telematic device connected to the vehicle's onboard diagnostics port to monitor and analyze CAN bus messages in real-time, using deep and machine learning algorithms to detect anomalies and generate alerts for immediate countermeasures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the CAN bus architecture is used for in-vehicle communication, then reliability and error handling are improved, but security is worsened due to lack of encryption and authentication
Solution Approach 1:
The patent introduces an intrusion detection system as an intermediary component that monitors CAN bus traffic without disrupting the original communication protocol. The system acts as a mediator between the existing CAN bus architecture and security requirements, analyzing messages for anomalies while maintaining the original reliable error handling capabilities of the CAN bus.
Solution Approach 2:
The patent replaces traditional mechanical/security approaches with electronic/software-based intrusion detection systems that use machine learning algorithms. Instead of modifying the physical CAN bus protocol, the system uses software agents and neural networks to detect security threats, substituting hardware-level security modifications with intelligent software monitoring.
2Adaptability or versatility
If connectivity is increased to enable remote access and telematics, then functionality is improved, but vulnerability to cyberattacks is worsened
Solution Approach 1:
The patent implements preliminary intrusion detection by training machine learning models with normal and malicious traffic patterns before deployment. The system pre-establishes baseline behavior patterns for CAN bus communication, enabling it to proactively detect deviations that indicate cyberattacks before they can compromise vehicle systems.
Solution Approach 2:
The intrusion detection system continuously monitors CAN bus traffic and provides real-time feedback about detected anomalies. The system analyzes incoming messages, compares them against learned patterns, and generates alerts or countermeasures when threats are detected, creating a closed-loop feedback mechanism that enhances security while maintaining connectivity.
3Ease of manufacture
If traditional intrusion detection methods are used, then implementation is simplified, but detection accuracy is reduced due to inability to detect unknown threats
Solution Approach 1:
The patent transforms the intrusion detection approach by changing from rule-based parameters to machine learning parameters. The system uses neural networks that can learn complex patterns in CAN bus traffic, detecting anomalies based on statistical deviations rather than predefined rules. This enables detection of previously unknown attack patterns while maintaining implementation feasibility through automated model training.
Data Source
AI summary
The present disclosure generally relates to intrusion detection systems for connected vehicles. In some embodiments, a telemetric device that listens to messages transmitted via a controller area network (CAN) bus is described. In some embodiments, the telemetric device is connected to an on-board diagnostic port. In some embodiments, the telemetric device sends an alert when one or more anomalies are present in one or more messages transmitted via the CAN bus.