Parallel Matrix Blame Score Detection for Transaction Security
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Solution Overview
Problem
Traditional computer systems face challenges in efficiently, quickly, and accurately detecting security breaches in electronic transactions, leading to delayed identification of points of compromise, which increases the likelihood of stolen information being misused.
Innovation Solution
The implementation of parallelization techniques for transaction data processing, including distributed computational matrices and blame score calculations across multiple nodes, enables rapid identification of likely points of compromise by dividing and distributing calculations, thereby enhancing detection efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional computer systems are used to analyze transaction data, then the system complexity remains low, but the detection speed and efficiency are insufficient
Solution Approach 1:
The patent segments the transaction data analysis into multiple independent computational tasks that can be executed in parallel. The system divides the large dataset into smaller chunks and distributes them across multiple processing nodes, allowing simultaneous analysis of different transaction subsets. This segmentation enables faster detection without requiring a single complex centralized system.
Solution Approach 2:
The patent transitions from sequential single-threaded processing to multi-dimensional parallel processing by introducing additional processing dimensions (multiple cores, multiple nodes, multiple threads). This dimensional expansion allows the system to analyze transactions along multiple pathways simultaneously, dramatically increasing detection speed while distributing system complexity across multiple simpler processing units.
2Measurement precision
If more computational resources are allocated to analyze vast amounts of transaction data, then detection accuracy improves, but processing time increases
Solution Approach 1:
The patent implements continuous parallel processing where multiple computational tasks are executed simultaneously and continuously without idle time. As soon as one processing node completes its analysis, its results are immediately integrated with ongoing analyses from other nodes. This continuous action maintains high detection accuracy through comprehensive analysis while minimizing processing time through uninterrupted parallel execution.
Solution Approach 2:
The patent performs preliminary filtering and preprocessing of transaction data before the main analysis phase. By pre-organizing data, pre-identifying potential anomalies, and pre-loading necessary information into optimized data structures, the system reduces the computational burden during the actual detection phase. This preliminary action enables faster processing without sacrificing detection accuracy.
3Ease of manufacture
If traditional sequential processing methods are used, then the system is easier to implement, but the detection of points of compromise is delayed
Solution Approach 1:
The patent introduces intermediary components such as data serialization layers, standardized communication protocols, and result aggregation middleware that facilitate parallel processing. These intermediaries provide simple, well-defined interfaces between processing nodes and the main system, making the implementation of complex parallel processing easier by abstracting away the complexity through standardized mediation layers.
Data Source
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AI summary
Techniques described herein include determining, for each account of several accounts, an element blame score (EBS) for each potential point of compromise (POC) of a plurality of POCs. Determining the EBS includes: representing the determinations as a computer computational matrix operation, dividing a matrix of the operation into blocks, and distributing the blocks among nodes for distributed computation. The method further includes determining an overall compromise blame score (OCBS) for each potential POC based on the determined EBS and determining whether the OCBSs have converged. If the OCBSs have converged, select at least one of the potential POCs as a likely POC based at least on the corresponding OCBS of the selected likely POC. If the determined OCBSs have not converged, updating the OCBS for each potential POC and the EBSs for each potential POC.