Anomaly Detection via Statistical Cell Annotation
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Solution Overview
Problem
Current systems lack effective methods to detect anomalous activity, such as fraud in online advertising, without prior knowledge of statistical distributions or a-priori information, and struggle to identify unusual patterns across multiple characteristics in real-time.
Innovation Solution
The system aggregates logged data into cells, annotates them with statistically derived measures of extremity, and identifies cells with a predefined number of annotations as anomalous, using techniques like distribution calculations and anomaly detection algorithms to flag unusual behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical distributions are calculated from sampled data to detect anomalies, then anomaly detection capability is improved, but system complexity increases
Solution Approach 1:
The system segments the anomaly detection process into distinct modules: data sampling module, distribution calculation module, anomaly scoring module, and threshold comparison module. Each module handles a specific aspect of the detection process, making the overall system more manageable and maintainable while achieving precise anomaly detection through coordinated operation of these specialized components
Solution Approach 2:
Statistical distributions serve as an intermediary layer between raw sampled data and anomaly detection decisions. The system calculates distributions from sampled data and uses these distributions as mediators to evaluate whether new data points represent anomalies, thereby simplifying the detection logic while improving measurement precision
2Measurement precision
If multiple characteristics are monitored for each data entry, then anomaly detection accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-calculating statistical distributions from historical sampled data before actual anomaly detection occurs. These pre-computed distributions are stored and reused for evaluating multiple data points, thereby reducing the processing time for each individual anomaly check while maintaining detection accuracy across multiple characteristics
Solution Approach 2:
The system merges the monitoring of multiple characteristics by computing a combined anomaly score that aggregates deviations across all monitored characteristics. This consolidation approach allows simultaneous evaluation of multiple characteristics without proportionally increasing processing time, as the merged scoring mechanism efficiently synthesizes information from all characteristics into a single anomaly assessment
Data Source
AI summary
In general, systems and methods for identifying anomalous activity are described. For example, systems and methods are described, in which patterns of unusual behavior can be identified by aggregating logged, or sampled, data into cells and annotating each cell with statistically derived measures of how extreme the cell is relative to, for example, historical behavior of corresponding characteristics or relative to, for example, behavior of characteristics from a general population. Cells that have more than a predefined number of such annotations can be identified as anomalous and can be investigated by a user or outright acted upon in an automatic, pre-defined way.