Automated Device Activity Analysis Using Generative Neural Networks
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Solution Overview
Problem
Entities face challenges in monitoring and analyzing device activity across networks for security purposes, particularly in identifying potential malfeasant activity, as existing solutions lack comprehensive forward and reverse analysis capabilities and effective user identity verification.
Innovation Solution
A collaborative system that uses artificial intelligence and machine learning to continuously analyze user device data, identify emerging patterns, and generate alerts for potential issues, employing a generative neural network approach to detect anomalies and initiate protective measures such as access restrictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive device activity monitoring and analysis is implemented, then network security is improved, but system complexity and computational resources increase
Solution Approach 1:
The system segments device activity monitoring into multiple independent analysis modules: forward analysis (real-time monitoring), reverse analysis (historical investigation), pattern recognition, and anomaly detection. Each module processes specific aspects of device activity independently, reducing overall system complexity while maintaining comprehensive security coverage.
Solution Approach 2:
The patent introduces an intermediary analysis layer that sits between raw device activity data and security decisions. This intermediary layer uses machine learning models to process and interpret device behavior patterns, transforming complex raw data into actionable security insights without requiring direct complex processing throughout the entire system.
2Measurement precision
If real-time and historical device activity analysis is performed, then threat detection capability is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary analysis by continuously learning and establishing baseline device behavior patterns in advance. Machine learning models are trained on historical device activity data to create reference profiles of normal behavior, enabling faster real-time threat detection by comparing current activity against pre-established baselines rather than analyzing all data from scratch.
Solution Approach 2:
The patent implements periodic analysis cycles where device activity is monitored continuously but analyzed at structured intervals. The system performs rapid forward analysis for immediate threats while conducting more comprehensive reverse analysis periodically, balancing detection precision with processing time requirements.
3Productivity
If automated pattern recognition and anomaly detection are implemented, then security response efficiency is improved, but algorithm complexity and false positive rates increase
Solution Approach 1:
The system dynamically adjusts analysis parameters and thresholds based on learned device behavior patterns. Machine learning models automatically modify detection sensitivity, time windows, and comparison criteria according to the specific device and context, reducing false positives while maintaining high detection efficiency without requiring fixed complex algorithms.
Data Source
AI summary
Embodiments of the present invention provide an innovative system, method, and computer program product for automated device activity analysis in both a forward and reverse fashion. A collaborative system for receiving data and continuously analyzing the data to determine emerging patterns associated with particular user devices is provided. The system is also designed to generate a historical query of user device touch points or interaction points with entity systems across multiple data vectors, and generate system alerts as patterns or potential issues are identified. Common characteristics of data may be used to detect patterns that are broadened in scope and used in a generative neural network approach.