On-Board IoV Attack Defense via Instruction Similarity Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing solutions for improving information security in Internet of Vehicle (IoV) systems have low inspection accuracy, making them vulnerable to attacks that can steal data or seize control of vehicles, posing risks to safety and security.
Innovation Solution
A method and apparatus that acquire and compare vehicle control instructions with an attack behavior knowledge base to determine similarity values, classifying instructions as safe, suspicious, or illegal, and processing them accordingly, with the option to send suspicious instructions to a cloud server for further analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud server or PC terminal is used to perform security analysis on external data, then information security of IoV is improved, but inspection accuracy remains low and response time is delayed
Solution Approach 1:
The patent pre-establishes an attack behavior knowledge base containing chained data sets from multiple on-board components before attacks occur. This preliminary preparation enables the system to perform rapid similarity comparisons against pre-computed attack patterns, improving inspection accuracy without requiring real-time complex analysis from cloud servers.
Solution Approach 2:
The patent introduces an on-board terminal as an intermediary between the vehicle's control system and the cloud server. This intermediary performs preliminary security analysis locally using the attack behavior knowledge base, filtering out obvious attacks before data is sent to the cloud, thereby improving inspection accuracy and reducing response time.
2Reliability
If cloud server performs security analysis on external data, then attack detection capability is enhanced, but response time is delayed and system complexity increases
Solution Approach 1:
The system performs preliminary security analysis locally at the on-board terminal using pre-computed attack behavior knowledge bases, enabling immediate detection and response to attacks without waiting for cloud server processing, thus significantly reducing response time.
Solution Approach 2:
The on-board terminal performs self-service security analysis by comparing received external data against the locally stored attack behavior knowledge base, enabling autonomous rapid response to attacks without relying on cloud server processing time.
3Measurement precision
If attack behavior knowledge base is built with chained data sets from multiple on-board components, then inspection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the attack behavior knowledge base into separate chained data sets corresponding to different on-board components (e.g., navigation, audio, video systems). This segmentation allows the system to manage complexity by processing each component's data independently through standardized similarity comparison procedures.
Solution Approach 2:
The patent creates a universal attack behavior knowledge base structure that can be applied across multiple on-board components using the same similarity comparison methodology. This multi-functional approach improves inspection accuracy through comprehensive coverage while managing complexity by reusing the same analytical framework.
Data Source
AI summary
The present disclosure discloses a method, apparatus, device, and storage medium for defending against attacks, which relate to the technical field of information security, and can be used in intelligent traffic or an autonomous driving scenario. The specific implementation solution is: acquiring an instruction set including at least one instruction for controlling vehicle state; comparing each instruction in the instruction set with at least one attack instruction in an attack behavior knowledge base respectively to determine a maximum similarity value corresponding to each instruction; and determining the type of the instruction and of the processing tactics for the instruction according to the maximum similarity value corresponding to each instruction and a preset similarity range.