User Behavior Anomaly Detection Using Location-Aware ML Baselines
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Solution Overview
Problem
Large enterprise organizations face challenges in quickly identifying internal threats due to the difficulty in capturing and analyzing user interactions and location data to detect anomalies, leading to a reactive approach that may not mitigate all impacts effectively.
Innovation Solution
A machine learning-based system continuously captures user interaction and location data to establish baseline patterns, using models like decision trees and neural networks to identify anomalies, and generates notifications for administrators to take proactive mitigation actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If reactive data assembly is used after incident detection, then quick response to known threats is achieved, but complete and accurate capture of user interactions is lost
Solution Approach 1:
The system performs preliminary data collection and pattern establishment continuously before incidents occur. Historical data is gathered and machine learning models are trained in advance to establish baseline user behavior patterns, enabling comprehensive analysis to be ready when incidents are detected.
Solution Approach 2:
The system maintains continuous data collection and analysis operations rather than performing one-time reactive assembly. User interaction data is captured ongoing, and machine learning models continuously update their understanding of normal behavior patterns, ensuring complete information availability when needed.
2Reliability
If continuous data collection and machine learning analysis is implemented, then early anomaly detection capability is improved, but system complexity and computational resources increase
Solution Approach 1:
The machine learning models perform self-training and self-adjustment using historical data without requiring manual reconfiguration. The system automatically updates its understanding of user patterns and anomaly thresholds, reducing the need for external intervention and simplifying operational complexity.
Solution Approach 2:
The system dynamically adjusts analysis parameters and model sensitivity levels based on accumulated data and detected patterns. Machine learning models automatically optimize their detection thresholds and feature weights, adapting to changing user behaviors and organizational contexts without manual parameter tuning.
3Measurement precision
If historical data is collected and used to train machine learning models, then anomaly detection accuracy is improved, but data storage and processing requirements increase
Solution Approach 1:
The system extracts and stores only the essential features and patterns from vast amounts of user interaction data rather than retaining all raw data. Machine learning models identify and retain only the most predictive features for anomaly detection, significantly reducing data storage requirements while maintaining high recognition accuracy.
Solution Approach 2:
The system applies different data retention and processing strategies to different data types and time periods. Recent data may be retained with higher precision for immediate pattern recognition, while historical data is processed and stored in aggregated form, optimizing the balance between data volume and analytical quality.
Data Source
AI summary
Arrangements for machine learning-based anomaly detection are provided. In some examples, historical data related to user interactions with enterprise organization resources and location may be received and used to train a machine learning model. Current user interaction data, as well as current location data, may be received. The current user interaction data and location data may be input to the machine learning model and the model may be executed to detect any anomalies in the data. If no anomalies are detected, the process may return to receive subsequent user interaction and location data for analysis. If an anomaly is detected, a notification may be generated and transmitted to an administrator computing device for display by the device.