Anomaly Detection in Digital Data Streams Using Pattern Frequency Thresholds
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Solution Overview
Problem
Current anomaly detection methods in digital data streams face challenges such as high false positive identifications, dimensionality issues, computational inefficiencies, and noise, making it difficult to automatically and intelligently detect anomalous events while reducing false positives.
Innovation Solution
The system analyzes digital data streams using user-specified or automatically determined analysis methods to identify patterns, calculates occurrence counts of characteristic patterns, and uses thresholds to determine anomalous data elements, updating 'normal' patterns over time to reduce false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional anomaly detection methods (statistical, distance-based, model-based) are used to detect anomalies in data streams, then anomaly detection capability is improved, but false positive identification rate increases
Solution Approach 1:
The system performs preliminary actions by continuously learning and storing patterns from normal data streams before anomaly detection is needed. This pre-learning phase allows the system to establish a baseline of normal behavior, enabling more accurate anomaly detection later while reducing false positives caused by lack of contextual understanding
Solution Approach 2:
The system implements feedback by continuously monitoring detected anomalies and using them to refine pattern recognition. The occurrence count mechanism provides feedback on pattern frequency, allowing the system to adjust its understanding of normal versus anomalous behavior over time, thereby improving detection accuracy while maintaining low false positive rates
2Reliability
If manual human inspection is used to detect anomalies, then false positive rate is reduced, but productivity decreases
Solution Approach 1:
The system performs self-service by automatically learning patterns from data streams and autonomously detecting anomalies without requiring continuous human intervention. The automated pattern recognition and anomaly detection mechanisms enable the system to maintain high reliability while achieving the processing speed and productivity of automated systems
Solution Approach 2:
The system replaces manual human inspection (mechanical process) with automated computational pattern recognition. This substitution maintains the high reliability of human analysis while achieving the speed and scalability of automated systems, resolving the contradiction between productivity and reliability
3Measurement precision
If complex data analysis methods are applied to high-dimensional data, then anomaly detection accuracy is improved, but computational efficiency deteriorates
Solution Approach 1:
The system extracts only the essential patterns and characteristics from high-dimensional data that are relevant for anomaly detection. By focusing on storing and analyzing only the significant patterns rather than processing all raw data, the system maintains high detection accuracy while significantly improving computational efficiency
Solution Approach 2:
The system segments the complex data analysis task into distinct phases: pattern extraction, pattern storage, and pattern matching. This segmentation allows each phase to be optimized independently, improving overall computational efficiency while maintaining the accuracy benefits of complex analysis methods
Data Source
AI summary
Systems and methods for determining whether or not one or pluralities of events, patterns, or data elements present within a given digital data stream should be delimited as anomalous. The system requires analyzes the data elements of the data stream using any acceptable user-specified, preset, or automatically determined analysis system. The results of the data processing, which are stored in a data storage structure such as a synaptic web or a data array for example, reveal synaptic paths (patterns) of characteristic algorithm values that function to individually define or delimit the selected data element(s) from the remainder of the original data stream.