Vehicle Log Threshold Analysis for Remote Abnormality Response
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Solution Overview
Problem
Vehicles with increasing software reliance and internet connectivity are vulnerable to cyberattacks, necessitating effective methods to detect and analyze abnormalities remotely.
Innovation Solution
A vehicle security operation center collects and sorts logs from vehicles, performs specific analysis on logs exceeding a threshold frequency, and responds by updating intrusion detection systems (IDS) or controlling electronic control units based on the analysis results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If logs are collected and analyzed from all vehicles continuously, then detection precision is improved, but use of energy and communication resources deteriorate
Solution Approach 1:
The system changes the parameter of log transmission frequency based on abnormality detection results. When abnormalities are detected, log collection and transmission frequency increases; when no abnormalities are detected, frequency decreases. This dynamic parameter adjustment optimizes energy consumption while maintaining detection precision.
Solution Approach 2:
The log collection system transitions from static continuous collection to dynamic conditional collection. The system adaptively adjusts its operation mode based on real-time abnormality detection, collecting logs frequently when threats are detected and reducing collection when the system is normal, thereby balancing detection precision with energy efficiency.
2Speed
If log collection frequency is increased to detect abnormalities faster, then response speed is improved, but use of energy and communication bandwidth deteriorate
Solution Approach 1:
The system dynamically changes the log collection interval parameter based on security risk levels. High-risk scenarios trigger frequent log collection for rapid detection, while low-risk scenarios use extended intervals to conserve energy, optimizing the balance between response speed and energy loss.
Solution Approach 2:
Instead of continuous log collection, the system implements periodic log collection with variable intervals. Collection occurs at regular intervals during normal operation and more frequently during suspected attack periods, reducing overall energy consumption while maintaining rapid response capability when needed.
3Measurement precision
If all collected logs are analyzed in detail, then detection precision is improved, but productivity of the analysis system deteriorates due to overwhelming data volume
Solution Approach 1:
The system extracts and prioritizes only the most critical log entries that indicate potential security threats. By filtering out routine normal operation logs and focusing analysis resources on suspicious activities, the system maintains high detection precision while preserving analysis throughput and productivity.
Solution Approach 2:
Different analysis depths are applied to different log types based on their security relevance. Critical security logs receive detailed analysis while routine operational logs receive minimal or no analysis. This localized quality approach ensures detection precision for threats while maintaining overall system productivity.
4Reliability
If the system responds to all detected abnormalities immediately, then reliability of security response is improved, but device complexity deteriorates due to multiple response protocols
Solution Approach 1:
The response system is segmented into standardized response protocols categorized by threat type and severity level. Each protocol handles specific scenarios with defined actions, making the complex response system modular and manageable while ensuring reliable and appropriate responses to different security events.
Solution Approach 2:
A universal response framework is implemented that can handle multiple types of security threats through standardized protocols. The system uses a common response architecture that adapts to different threat scenarios, reducing overall system complexity while maintaining reliable responses across diverse security events.
Data Source
AI summary
A method of detecting and analyzing a vehicle abnormality by a vehicle security operation center includes collecting logs from a vehicle and sorting the collected logs by each type. The method also includes determining whether a frequency of the logs sorted by each type is a threshold value or more and performing a specific analysis on logs determined that a frequency is greater than or equal to the threshold value. The method additionally includes responding to control the vehicle to perform one or more operations based on a result of the specific analysis.