Heterogeneous Feature Integration for Device Behavior Analysis
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Solution Overview
Problem
Current deep learning methods are inadequate for predicting device failure in computing systems, as they struggle with integrating heterogeneous features and detecting anomalies without sufficient training data or expert knowledge.
Innovation Solution
The implementation of heterogeneous feature integration for device behavior analysis (HFIDBA) using a processor to represent devices as vectors, extracting static, temporal, and deep embedded features, and determining device status through a neural network-based system that builds prediction models from historical data to forecast future server failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning methods are used for device failure prediction, then prediction capability is improved, but the ability to integrate heterogeneous features deteriorates
Solution Approach 1:
The patent segments heterogeneous features into distinct categories (static features, temporal features, communication features) and processes each category through dedicated processing pathways in the neural network architecture. This segmentation allows the system to handle diverse feature types effectively while maintaining overall prediction accuracy.
Solution Approach 2:
The patent employs a composite feature representation approach, combining multiple heterogeneous feature types into a unified device state vector. This composite structure integrates static device attributes, temporal behavior patterns, and communication characteristics into a single comprehensive representation that the neural network can process effectively.
2Device complexity
If traditional deep learning methods are used, then model complexity is reduced, but the ability to detect anomalies without sufficient training data deteriorates
Solution Approach 1:
The patent incorporates preliminary action by pre-processing device data into structured feature representations before feeding them to the neural network. Static features are extracted and encoded in advance, temporal patterns are pre-computed, and communication features are organized into standardized formats. This preliminary structuring enables effective anomaly detection even with limited training data.
3Measurement precision
If heterogeneous feature integration is implemented, then device behavior analysis capability is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal neural network architecture that handles multiple feature types through a unified processing framework. The same core neural network structure processes static features, temporal features, and communication features, reducing overall system complexity despite the diversity of input data. This multi-functional approach allows the system to analyze various device behaviors without requiring separate specialized models for each feature type.
Data Source
AI summary
Systems and methods for implementing heterogeneous feature integration for device behavior analysis (HFIDBA) are provided. The method includes representing each of multiple devices as a sequence of vectors for communications and as a separate vector for a device profile. The method also includes extracting static features, temporal features, and deep embedded features from the sequence of vectors to represent behavior of each device. The method further includes determining, by a processor device, a status of a device based on vector representations of each of the multiple devices.