Zero-Knowledge Sample Selection for Iterative Malware Training
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing malware detection systems face challenges in efficiently training machine learning models due to the use of large, unstructured threat data that can lead to scalability issues, model bias, and resource-intensive training processes, particularly when dealing with clustered data where prior knowledge of clustering is lacking.
Innovation Solution
An iterative, zero-knowledge training approach is employed to select a representative dataset using clustering techniques, reducing the need for full dataset training, thereby addressing model skew and bias, and improving model performance through iterative selection and weighting of samples based on classification differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large, unstructured threat data is used for training machine learning models, then model coverage and detection capability are improved, but scalability issues and resource-intensive training processes occur
Solution Approach 1:
The patent extracts and removes redundant and duplicate samples from the large threat data repository, keeping only representative samples from each malware cluster. This extraction process reduces the training dataset size while maintaining detection capability, directly resolving the contradiction between comprehensive coverage and training efficiency.
Solution Approach 2:
The patent segments the large, unstructured threat data into distinct clusters based on malware characteristics. By organizing data into clusters and selecting representative samples from each, the system transforms an unwieldy monolithic dataset into manageable segments, improving scalability and reducing resource requirements while maintaining comprehensive detection coverage.
2Measurement precision
If full dataset training is performed, then model accuracy is maximized, but computational resources and training time are excessively consumed
Solution Approach 1:
The patent applies partial action by training the model on a carefully selected subset of representative samples rather than the full dataset. Through iterative selection and weighting of samples based on classification differences, the system achieves near-optimal accuracy using only a fraction of the data, significantly reducing computational resource consumption while maintaining model performance.
Solution Approach 2:
The patent changes the parameter of sample selection from uniform random sampling to weighted sampling based on classification differences. By adjusting the selection weighting parameter iteratively, the system optimizes which samples contribute most to model accuracy, achieving high precision with reduced computational resources.
3Reliability
If clustered data is used for training, then model performance on specific malware types is improved, but model bias and skew toward certain clusters occur
Solution Approach 1:
The patent applies local quality by ensuring each malware cluster is adequately represented in the training set with appropriate sample weighting. Rather than treating all samples uniformly, the system adjusts the quality and weight of samples locally based on their cluster affiliation and classification difficulty, maintaining both specialized performance and overall data balance.
Solution Approach 2:
The patent introduces dynamics through iterative re-weighting of sample selections based on classification differences. The sample weights are not static but dynamically adjusted across training iterations to balance cluster representation, preventing bias toward dominant clusters while maintaining performance on all malware types.
4Adaptability or versatility
If representative dataset selection is performed without prior clustering knowledge, then zero-knowledge training is achieved, but additional computational steps for clustering and weighting are required
Solution Approach 1:
The patent applies self-service by enabling the training system to automatically perform clustering and sample selection without requiring prior external knowledge of malware clusters. The system autonomously identifies clusters, selects representative samples, and weights them iteratively based on classification performance, making the process self-sufficient and adaptable to unknown threat landscapes.
Solution Approach 2:
The patent implements feedback loops where classification results are fed back into the sample selection and weighting process. The system uses classification differences to adjust sample weights in subsequent iterations, creating a self-correcting mechanism that improves representation without requiring prior clustering knowledge, thereby managing complexity through iterative refinement.
Data Source
AI summary
A method may select, from a training data repository comprising a plurality of samples with known classifications, an initial training dataset comprising a second plurality of samples. A method may provide, as an input to a classification model, feature vectors associated with the initial training dataset and may train the classification model using the feature vectors. A method may determine a classification of each sample of a third plurality of samples using the classification model. A method may determine a difference between the determined and the known classification for each sample. A method may determine a selection weighting for each sample for based on the difference between the determined classification and the known classification. A method may select a subset from the from the third plurality of samples based on the determined selection weighting. A method may train the classification model using feature vectors associated with the subset.