Trustworthiness Classifier Segmentation for Organizational Accuracy
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Solution Overview
Problem
Traditional trustworthiness classifiers often lead to false positives and false negatives due to their broad, generalized approach, failing to account for the specific nuances of individual organizations within a vendor's clientele, resulting in reduced accuracy when applied to files encountered by those organizations.
Innovation Solution
The method involves selecting subsets of training data based on specific organizational characteristics, such as size, industry, or geographic region, to tailor trustworthiness classifiers for each organization, improving classification accuracy by using data relevant to that organization's context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a traditional trustworthiness classifier is trained using a broad set of training data from all organizations, then the classifier can be broadly tailored for general clientele, but it fails to account for specific organizational nuances and loses accuracy when applied to files encountered by certain organizations
Solution Approach 1:
The patent segments the broad training data into multiple subsets, each corresponding to a specific organization or organizational type. Instead of using a single unified classifier for all organizations, the system divides the training data by organizational characteristics (e.g., industry, size, geographic region) and creates specialized classifiers for each segment, thereby resolving the contradiction between broad applicability and specific accuracy
Solution Approach 2:
The patent applies local quality by tailoring the training data composition to match specific organizational characteristics. Each organization receives a customized classifier trained on training data that reflects its unique context, industry, and operational patterns, rather than applying a uniform classifier to all organizations. This ensures high classification accuracy for each specific organizational context while maintaining the ability to serve diverse clientele
2Measurement precision
If a trustworthiness classifier is customized for each specific organization using organization-specific training data, then classification accuracy for that organization improves, but the complexity of generating and maintaining multiple classifiers increases
Solution Approach 1:
The patent implements a universal classifier framework that can serve multiple organizations simultaneously. The system uses a common classifier architecture and training methodology that can be applied across different organizations by simply changing the training data subset. This multi-functional approach allows the same classifier structure to be customized for various organizations without requiring entirely separate systems, thereby reducing overall complexity
Solution Approach 2:
The patent changes the parameters of the training data rather than creating fundamentally different classifiers for each organization. By adjusting which training data samples are included based on organizational characteristics (e.g., selecting data from the same industry, size range, or geographic region), the system achieves organization-specific accuracy while maintaining a consistent classifier framework, thus avoiding the complexity of managing multiple disparate classifier systems
Data Source
AI summary
The disclosed computer-implemented method for improving the classification accuracy of trustworthiness classifiers may include (1) identifying a set of training data that is available for training trustworthiness classifiers used to classify computing resources as clean or malicious, (2) selecting, based at least in part on a characteristic of a specific organization, a subset of training data from the set of training data that is available for training trustworthiness classifiers, (3) training a trustworthiness classifier for the specific organization using the subset of training data selected based at least in part on the characteristic of the specific organization, and then (4) applying the trustworthiness classifier to at least one computing resource encountered by the specific organization to classify the computing resource as clean or malicious. Various other methods, systems, and computer-readable media are also disclosed.