Abnormal Frame Detection Using Receipt Interval Accumulation
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Solution Overview
Problem
Existing methods for determining abnormal data frames in an in-vehicle communication system face challenges due to errors caused by time stamp resolution and memory consumption, and are unable to accurately identify which data frame is abnormal.
Innovation Solution
An abnormal frame determination device that calculates receipt intervals between data frames, updates a determination value based on these intervals, and determines an abnormal frame when the value reaches a limit, allowing for precise identification of abnormal data frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If time stamp based abnormal frame determination is used, then the determination process is simple, but errors occur due to time stamp resolution limitations
Solution Approach 1:
The patent introduces a determination value as an intermediary parameter that accumulates receipt interval information over multiple frames. Instead of directly using timestamp differences which are limited by resolution, the determination value aggregates multiple measurements, thereby improving measurement precision while maintaining a relatively simple determination process.
Solution Approach 2:
The patent performs preliminary accumulation of receipt interval information in the determination value before making the abnormal frame determination. By pre-processing and accumulating data from multiple frames, the system improves measurement accuracy without requiring complex real-time analysis when an abnormal frame is detected.
2Measurement precision
If multiple data frames are stored for analysis, then abnormal frame identification accuracy improves, but memory consumption increases
Solution Approach 1:
The patent extracts only the essential information needed for abnormal frame determination by using a determination value that accumulates receipt interval data. Instead of storing complete data frames or extensive metadata, the system extracts and accumulates only the timing information necessary for detection, thereby improving identification accuracy while minimizing memory consumption.
Solution Approach 2:
The patent applies local quality by maintaining detailed receipt interval information only for the specific parameter needed (timing between frames) rather than storing complete frame data. The determination value focuses locally on the critical timing metric, achieving high identification accuracy without the memory cost of storing entire data frames or comprehensive analysis data.
3Reliability
If receipt interval threshold is set low for high sensitivity, then more abnormal frames are detected, but false positives increase
Solution Approach 1:
The patent performs preliminary accumulation of receipt interval information in the determination value before making determination. This pre-processing allows the system to establish a more reliable baseline of normal variation, enabling higher sensitivity detection while reducing false positives through accumulated evidence rather than single-frame thresholds.
Solution Approach 2:
The determination value provides feedback by accumulating receipt interval information over multiple frames, allowing the system to distinguish between normal variations and genuine abnormalities. This feedback mechanism enables the system to maintain high sensitivity while reducing false positives through pattern recognition based on accumulated data.
Data Source
AI summary
An abnormal frame determination device receives data frames which are transmitted by an electronic control unit at a predetermined period. A receipt interval between a first data frame and a second data frame is calculated. The second data frame is received immediately before receipt of the first data frame. A predetermined value is added to a determination value stored in a storage when the receipt interval is shorter than a threshold. It is determined that the second data frame is an abnormal frame when the determination value reaches a limit value and the receipt interval is longer than a proximity receipt interval that is a criterion for determining whether the first data frame and the second data frame are received in proximity to each other.