ECU Binary File Categorization Using Locality-Sensitive Hashing
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Solution Overview
Problem
Existing binary file classification techniques are complex, time-consuming, and prone to mislabeling, especially when dealing with trimmed or incomplete files, and there is a need for efficient AI/ML-based solutions for vehicle ECUs to manage diagnostics, firmware analysis, and tuning.
Innovation Solution
A technique using locality-sensitive hashes and fuzzy subsequence matching, combined with AI/ML algorithms, for categorizing executable binary files, focusing on data organization and similarity, and employing proprietary hash functions to enhance classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If classical classification techniques based on characteristic patterns are used, then classification can be performed, but the process is complex and time-consuming
Solution Approach 1:
The patent replaces classical mechanical pattern-matching classification with a neural network-based AI system. The neural network automatically learns classification rules from training data, eliminating the need for manual dataset preparation and characteristic pattern definition. This substitution dramatically reduces both the time required for categorization and the complexity of data preparation while maintaining high classification accuracy.
2Measurement precision
If classical classification techniques are used, then categorization can be performed, but mislabeling occurs due to misleading data sequences
Solution Approach 1:
The patent implements a feedback mechanism through the neural network's learning process. The system is trained on labeled datasets where the expected classifications are known. During training, the neural network receives feedback in the form of prediction errors and adjusts its internal parameters to minimize these errors. This feedback loop enables the system to learn from mistakes and continuously improve classification accuracy, reducing mislabeling even when confronted with misleading data sequences.
3Adaptability or versatility
If AI/ML solutions are implemented for ECU management, then sophisticated analysis capabilities are achieved, but training and implementation time increases
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network on comprehensive datasets before deployment. The system is trained in advance on diverse binary file types and patterns that may be encountered in ECU diagnostics and analysis. This pre-training establishes a robust foundation of knowledge that enables the AI system to perform sophisticated analysis tasks immediately upon deployment, reducing the need for extensive on-site training and accelerating implementation time while maintaining high adaptability.
Data Source
AI summary
A technique of categorization of binary executable files comprising the following steps:a) a starting step of analysis of input and training data for containing semi-organized, partially monotonic sequences;b) a preprocessing step wherein potential sequences are discarded or accepted on the basis of preset statistical criteria;c) an encoding step of said accepted sequences with metadata;d) a storing step of the encoded sequences, wherein said sequences are stored as part of the data sequences digest containing information describing the spatial organization of the sequences, the monotonicity features thereof and other features valuable for approximate matching;e) a computing step of locality-sensitive hashes corresponding to said input and training data files.