Micro-Clustering System for Malware Detection Signatures
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Solution Overview
Problem
Existing clustering algorithms form large, impure clusters that are challenging to convert into effective signatures for malware detection, requiring constant parameter tuning and struggling with scalability and precision, especially in big data environments.
Innovation Solution
A system and method for micro-clustering that selects a vantage point to sort objects into smaller, more precise clusters based on proximity, using a 'laser cutting' strategy and iteratively changing vantage points to form smaller, purer micro-clusters, which are then used to derive signatures for improved malware detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing clustering algorithms are used to group objects, then clusters are formed, but the clusters are large and impure, reducing detection precision
Solution Approach 1:
The patent divides the clustering process into multiple passes, where each pass segments the data into smaller micro-clusters. Instead of forming one large cluster, the algorithm iteratively creates multiple smaller clusters, each with higher purity. This segmentation approach directly resolves the contradiction by breaking down large impure clusters into smaller pure ones.
Solution Approach 2:
The patent introduces a new dimension to traditional clustering by using vantage points and measuring distances from these points. This dimensional change allows objects to be clustered based on their proximity to vantage points rather than traditional distance metrics, enabling the formation of smaller, more precise micro-clusters that maintain high purity while reducing size.
2Ease of operation
If traditional clustering algorithms are used, then clusters are formed, but constant parameter tuning is required, increasing operational complexity
Solution Approach 1:
The patent implements self-service through its iterative micro-clustering process. The algorithm automatically adjusts and refines clusters across multiple passes without requiring external parameter tuning. The system serves itself by identifying vantage points and forming micro-clusters based on intrinsic data properties, eliminating the need for constant manual intervention and parameter adjustment.
Solution Approach 2:
The patent applies dynamics by making the clustering process adaptive and iterative. Rather than using fixed parameters, the algorithm dynamically adjusts cluster formations across multiple passes, selecting vantage points and forming micro-clusters based on the current state of the data. This dynamic approach reduces operational complexity by eliminating the need for manual parameter tuning.
3Productivity
If clustering algorithms process big data, then clusters are formed, but scalability is challenged, reducing productivity
Solution Approach 1:
The patent segments big data into manageable micro-clusters across multiple passes, making the processing of large volumes of data scalable. By dividing the data into smaller chunks that can be processed independently, the algorithm maintains high productivity even when handling big data, avoiding the scalability challenges of traditional clustering methods.
Solution Approach 2:
The patent employs partial action by forming micro-clusters that capture the essential patterns in the data without requiring complete processing of all possible relationships. This approach allows the algorithm to achieve effective clustering results on big data with reduced computational overhead, improving scalability and productivity.
Data Source
AI summary
A computer-implemented system and method of clustering a universe of featurized objects into micro-clusters includes selecting a vantage point having a feature vector; computing, for the featurized objects in the universe, respective distances from the vantage point, and sorting the featurized objects into a sorted container based on their distances from the vantage point; clustering adjacent objects into a plurality of micro-clusters based on determining that objects have a distance from a next adjacent object less than a maximum distance; and storing the micro-clusters onto a tangible computer-readable medium to modify operation of a computing apparatus based on objects in the micro-clusters.