Approximate Ground Truth Refinement for Malware Classifier Evaluation
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Solution Overview
Problem
The evaluation of classifier models is challenging when using small or low-quality reference datasets lacking ground truth labels, leading to inaccurate or misleading results, especially in fields where obtaining high-quality reference datasets is costly or impractical.
Innovation Solution
The development of an approximate ground truth refinement (AGTR) framework allows for the construction of a ground truth refinement with specified error bounds from a reference malware dataset, enabling the determination of lower bounds on precision and upper bounds on recall and accuracy without requiring reference labels, thereby evaluating classifier models more accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If reference datasets are used for evaluating classifier models, then evaluation can be performed, but the results are inaccurate or misleading when reference datasets are small, lack diversity, or have imbalanced class distribution
Solution Approach 1:
The patent introduces an approximate ground truth refinement (AGTR) as an intermediary construct that mediates between the imperfect reference dataset and the classifier evaluation. The AGTR provides a more reliable evaluation framework by refining the approximate ground truth with error bounds, allowing accurate evaluation even when reference datasets are small or imbalanced.
Solution Approach 2:
The patent changes the parameter of ground truth quality by introducing error bounds and refinement levels. Instead of treating ground truth as absolute or completely absent, the system parameterizes the quality of ground truth through error bounds, enabling evaluation to proceed with quantified uncertainty even when reference datasets are limited.
2Measurement precision
If ground truth reference labels are obtained for large datasets, then accurate evaluation is possible, but it is costly or impractical in many fields
Solution Approach 1:
The patent performs preliminary action by constructing an approximate ground truth refinement before full evaluation occurs. This preliminary AGTR construction with error bounds preparation enables subsequent evaluation to proceed efficiently without requiring time-consuming manual annotation of entire datasets, as the refinement framework is established in advance.
Solution Approach 2:
The patent uses cheap, computationally-generated approximate ground truth refinements instead of expensive manual annotations. These AGTRs can be generated quickly through automated processes and discarded or regenerated as needed, replacing the need for costly, time-intensive human labeling while maintaining sufficient evaluation accuracy.
3Ease of operation
If small reference datasets are used due to infeasibility of obtaining ground truth, then evaluation can proceed, but the results are unreliable
Solution Approach 1:
The patent introduces dynamics by making the evaluation framework adaptive to the quality and size of available reference datasets. The AGTR construction dynamically adjusts error bounds and refinement levels based on the input data characteristics, allowing the system to maintain reliability across varying dataset conditions rather than requiring fixed, ideal conditions.
Solution Approach 2:
The patent implements feedback through error bounds that provide continuous information about the reliability of the evaluation. The AGTR framework feeds back uncertainty measurements to the evaluation process, allowing users to understand the confidence level of results and adjust accordingly, thereby maintaining reliability even with limited reference data.
Data Source
AI summary
Disclosed are methods and apparatuses for classifier evaluation. The evaluation involves constructing a ground truth refinement having a degree of error within specified bounds from a malware reference dataset as an approximate ground truth refinement. The evaluation further involves using the approximate ground truth refinement to determine at least one of: a lower bound on precision or an upper bound on recall and accuracy. The evaluation further involves evaluating a classifier by evaluating at least one of a classification method or clustering method by examining changes to the upper bound and/or the lower bound produced by the approximate ground truth refinement.