In-Vehicle Network Threat Detection via Code Execution Deviation
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Solution Overview
Problem
As vehicles become increasingly connected to wireless networks, especially in autonomous systems, there is a heightened risk of cyber attacks, necessitating effective detection and prevention mechanisms within in-vehicle networks to ensure cybersecurity.
Innovation Solution
A system and method for detecting cyber threats by analyzing deviations in executable code execution using whitelists and blacklists, employing a rule engine to associate chains of events with threats, and taking actions such as blocking or preventing malicious code execution, with features like digital signature verification and confidence-level-based responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If vehicles are connected to wireless networks for autonomous operation, then functionality and connectivity are improved, but vulnerability to cyber attacks increases
Solution Approach 1:
The system performs preliminary actions by establishing whitelists and blacklists of expected executable code behaviors before potential cyber attacks occur. These reference execution behaviors are created during normal operation and stored for later comparison, enabling the system to proactively prepare detection mechanisms rather than reacting to threats after they occur.
Solution Approach 2:
The system implements continuous feedback loops where actual executable code execution is constantly monitored and compared against reference execution behaviors. When deviations are detected, the system provides feedback by generating alerts, blocking malicious code, or resetting ECUs. This closed-loop feedback mechanism enables real-time detection and response to cyber threats while maintaining normal vehicle operations.
2Reliability
If monitoring and detection mechanisms are implemented in in-vehicle networks, then security is improved, but system complexity increases
Solution Approach 1:
The system extracts and isolates specific monitoring functions into separate software sensors that can intercept and analyze system calls independently. Each software sensor focuses on detecting particular types of deviations in executable code execution, allowing the complex security monitoring task to be divided into manageable, specialized components rather than requiring a monolithic complex system.
Solution Approach 2:
The detection system implements universal mechanisms that can identify multiple types of cyber threats using the same core comparison approach. The whitelists and blacklists serve as universal reference structures that can detect various forms of executable code exploitation across different ECUs and vehicle systems, reducing overall system complexity through standardized detection methods.
3Measurement precision
If real-time detection of executable code deviations is performed, then threat detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system applies partial monitoring by focusing detection efforts on specific high-risk areas such as ECUs with external interfaces (telematics, multimedia, driver assistance systems). Rather than uniformly monitoring all executable code across the entire vehicle network, the system concentrates computational resources on partially monitoring the most vulnerable components, achieving effective threat detection with reduced processing overhead.
Solution Approach 2:
The system performs preliminary establishment of whitelists and blacklists during normal operation, creating reference execution behaviors in advance. This preliminary action allows the system to have detection criteria ready before threats occur, enabling faster real-time comparison and decision-making without requiring complex analysis during critical threat detection moments.
4Adaptability or versatility
If ECUs with external interfaces are used for high-performance operations, then functionality is improved, but security risk increases
Solution Approach 1:
The system applies local quality by implementing differentiated security monitoring strategies for different ECUs based on their specific functions and risk profiles. ECUs with external interfaces (telematics, multimedia, driver assistance) receive enhanced monitoring and more stringent detection rules, while internal ECUs use standard monitoring. This localized approach tailors security measures to the specific quality and risk level of each ECU.
Solution Approach 2:
The system introduces software sensors as intermediary components between ECUs with external interfaces and the in-vehicle network. These software sensors act as mediators that intercept and analyze system calls from high-risk ECUs before they can execute potentially malicious code, providing an additional layer of protection without preventing the ECUs from performing their high-performance functions.
Data Source
AI summary
A system and method for detecting cyber threats in a vehicle may detecting an event related to exploitation of a component connected to an in-vehicle network based on a deviation of execution of executable code from a reference execution behavior. A deviation may be detected based on a set of whitelists and blacklists. An event related to a deviation may be recorded.