Multi-layer anomaly detector for voluminous security data
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Solution Overview
Problem
Organizations struggle with security breaches due to the vast amount of voluminous data, which requires significant manual effort for complex correlations, and existing automated solutions are inadequate in identifying and resolving complex security compromises.
Innovation Solution
A multi-layer anomaly detector system that includes a clusterer, a forecaster, a statistics generator, and an ensemble detector to generate anomaly lists and alerts, focusing on identifying and surfacing the most critical security risks from voluminous data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis is used to process voluminous security data, then measurement precision of security risks is improved, but productivity is deteriorated due to the vast amount of data requiring significant manual effort
Solution Approach 1:
The system segments the analysis process into three independent anomaly detectors (clusterer, forecaster, statistics generator) that each process data independently and generate separate anomaly lists. These detectors work in parallel to reduce processing time while maintaining comprehensive analysis coverage, resolving the contradiction between thorough analysis and processing speed.
Solution Approach 2:
Instead of analyzing all voluminous events in detail, the system performs partial analysis by generating anomaly lists that identify only the most suspicious events. The ensemble detector then combines these partial results to surface the most critical security risks, enabling fast processing without sacrificing measurement precision for the most important threats.
2Measurement precision
If multiple anomaly detectors are used to improve security risk identification, then measurement precision is improved, but device complexity is deteriorated
Solution Approach 1:
The complex detection task is segmented into three specialized detectors (clusterer for pattern recognition, forecaster for temporal analysis, statistics generator for baseline comparison), each with a specific function. This segmentation makes the overall complex system manageable by breaking it into simpler, independent components that can be developed and maintained separately.
Solution Approach 2:
The ensemble detector acts as an intermediary that combines the outputs of the three anomaly detectors. It receives anomaly lists from each detector, integrates the results, and generates a unified ensemble anomaly list. This intermediary layer simplifies the complexity by providing a single interface for result aggregation while allowing the underlying detectors to remain independent and modular.
3Measurement precision
If all events are analyzed in detail, then measurement precision is improved, but loss of time is deteriorated due to the voluminous nature of the data
Solution Approach 1:
The system performs partial analysis by generating anomaly lists that flag only potentially suspicious events rather than analyzing every event in detail. The clusterer, forecaster, and statistics generator each produce condensed anomaly lists that highlight unusual patterns, allowing the system to quickly identify candidate events without exhaustive analysis of all voluminous data.
Solution Approach 2:
The three anomaly detectors perform preliminary filtering and analysis before final security risk assessment. By pre-processing the voluminous data through multiple detection lenses and generating anomaly lists in advance, the system reduces the time required for detailed analysis of individual events, as the most suspicious candidates are already identified and prioritized.
Data Source
AI summary
Multi-layer anomaly detection identifies and issues alerts to focus limited resources on the most concerning activities detected in voluminous data. A multi-layer anomaly detector includes a clusterer, a forecaster, and a statistics generator to respectively generate a first, second, and third anomaly lists for input data indicating voluminous events relative to one or more domains, such as access to computing devices; access to real estate; or financial transactions. An ensemble detector may generate an ensemble anomaly list indicating a subset of the voluminous events based on the first, second, and third anomaly lists. The lists may indicate anomaly (e.g., security risk) scores. The ensemble anomaly list may combine the anomaly (e.g., risk) scores in the first, second, and third anomaly lists. An identifier may generate an alert for the subset of events, e.g., with relative security risk scores for the relevant individuals, entities, and/or events.