Vehicle Function Restriction Using Fleet Cyberattack Risk Matching
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Solution Overview
Problem
Current countermeasures for cyberattacks in in-vehicle networks are reactive, detecting anomalies after an attack has occurred, which allows the vehicle to be compromised before the issue is addressed, and may fail to detect unknown attack patterns in a timely manner, leading to potential serious damage.
Innovation Solution
An information processing device that collects incident information from multiple vehicles and determines the risk level of vehicle functions based on matching criteria, generating function restriction commands to prevent attacks by restricting vehicle functions when the risk level exceeds a predetermined criterion, thereby preventing intrusion and minimizing damage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive anomaly detection is used to identify cyberattacks, then the system can detect known attack patterns, but the vehicle is already compromised before detection and unknown attack patterns cannot be detected timely
Solution Approach 1:
The system performs preliminary actions by collecting incident information from multiple vehicles and pre-calculating risk levels for different vehicle functions before attacks occur. The server maintains a database of incident information and pre-establishes risk assessment models, enabling proactive identification of potential vulnerabilities across the vehicle fleet before actual attacks exploit them.
Solution Approach 2:
The system creates equipotentiality by sharing risk level information across the entire vehicle fleet through the server. All vehicles receive equivalent protective intelligence about potential threats based on aggregated incident data, ensuring that no single vehicle is left vulnerable to known attack patterns while maintaining uniform security standards across the network.
2Reliability
If vehicle functions are restricted to prevent cyberattacks, then the vehicle is protected from potential threats, but the convenience of using vehicle systems is reduced
Solution Approach 1:
The system applies dynamic function restriction where vehicle functions are not permanently disabled but are conditionally restricted based on real-time risk level assessments. The server dynamically adjusts which functions are restricted and for how long, allowing normal operation when risk is low while imposing restrictions only when specific high-risk conditions are detected, thus balancing security with usability.
Solution Approach 2:
The system applies local quality by restricting only specific vehicle functions that are associated with detected risk levels rather than disabling all functions. The server identifies which particular functions pose security risks based on incident information and applies restrictions selectively to those specific functions, leaving other non-risky functions fully operational to maintain convenience.
3Measurement precision
If incident information from multiple vehicles is collected and analyzed, then the risk level can be accurately determined, but the system complexity increases
Solution Approach 1:
The system uses an intermediary server that acts as a mediator between multiple vehicles and the risk assessment process. The server collects incident information from various vehicles, processes this data centrally using pre-established algorithms, and distributes risk level information back to vehicles. This intermediary approach simplifies individual vehicle complexity while enabling accurate fleet-wide risk measurement through centralized data aggregation and analysis.
Data Source
AI summary
An information processing device includes: a processor; and a memory including at least one set of instructions that, when executed by the processor, causes the processor to perform operations. The operations include: obtaining incident information about an incident of a cyberattack that occurred in a vehicle; obtaining first vehicle information about a state of a first vehicle; storing, in the memory, the incident information and the first vehicle information; determining a risk level of a vehicle function of the first vehicle, based on a degree of matching between the incident information and the first vehicle information stored in the memory, the vehicle function of the first vehicle being one among one or more vehicle functions of the first vehicle; generating a function restriction command for restricting the vehicle function, when the risk level is higher than a first criterion; and outputting the function restriction command.