Target System Anomaly Control Through Matrix Partitioning
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Solution Overview
Problem
Anomaly detection mechanisms in complex target systems like communications networks or industrial processes are computationally heavy and memory-consuming due to the large volume of monitored variables, making efficient analysis challenging.
Innovation Solution
A method involving partitioning a matrix of monitored variables into submatrices, standardizing each submatrix, and performing anomaly detection independently on these submatrices while aggregating results efficiently to reduce memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If anomaly detection is performed on the entire large matrix of monitored variables, then comprehensive anomaly detection coverage is achieved, but memory consumption and computational load increase significantly
Solution Approach 1:
The patent divides the large matrix of monitored variables into multiple submatrices based on dimensional groupings (e.g., time windows, device types, network segments). Each submatrix is processed independently for anomaly detection, then results are aggregated. This segmentation reduces the memory footprint of individual processing operations while maintaining comprehensive coverage across all dimensions through systematic aggregation of results.
2Measurement precision
If the entire dataset is processed for anomaly detection, then detection accuracy is improved, but processing time increases
Solution Approach 1:
The dataset is segmented into submatrices that can be processed in parallel or sequentially with reduced computational overhead. Each submatrix retains sufficient contextual information for accurate local anomaly detection, and the aggregation of results across submatrices provides comprehensive system-wide detection accuracy.
Solution Approach 2:
Instead of processing the entire matrix simultaneously, the method applies anomaly detection to subsets of data (partial action) in a structured sequence. This approach processes data in manageable portions while ensuring all critical dimensions are covered through systematic aggregation, reducing total processing time without sacrificing detection accuracy.
Data Source
AI summary
A computer implemented method for controlling a target system. The method is implemented by obtaining a first matrix (310) comprising a plurality of monitored variables as columns and a plurality of observations of the monitored variables measured from the target system as rows, wherein each row is associated with one or more dimensions: partitioning (402) the first matrix into a plurality of submatrices (412-416), each submatrix comprising a subset of rows of the first matrix; separately processing the submatrices by standardizing (403) values of the submatrix (412-416) to obtain standardized submatrix (422-426); and processing (404) the standardized submatrix (422-426) by performing anomaly detection on the values of the standardized submatrix and by aggregating results of the anomaly detection by the respective one or more dimensions to a result matrix (430); and outputting the result matrix (430) or information derived from the result matrix (430) for the purpose of controlling the target system.