IoT Device Identity Augmentation via Metric Segmentation
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Solution Overview
Problem
The Internet-of-Things (IoT) landscape lacks central standards, leading to deployment, architecture, analytics, and security challenges due to the fragmentation of devices and communication protocols, making it difficult to automate systems and ensure interoperability across diverse IoT environments.
Innovation Solution
The implementation of an Information Handling System (IHS) that receives high-level and low-level metrics, determines device profiles using threshold values, and identifies devices within the IoT network by correlating these metrics, enabling device classification, anomaly detection, and security enhancements through device identity augmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If device metrics are collected and analyzed to identify device classes, then device identification accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments device identification into multiple independent components: high-level metrics collection, low-level metrics collection, threshold value storage, and matching operations. Each component processes specific aspects of device characteristics separately, making the overall complex task manageable and systematic.
Solution Approach 2:
The patent implements nested processing where low-level metrics are collected and processed within the framework of high-level metrics. The device profile matching operates within the broader context of collected metrics, creating a hierarchical structure that manages complexity through organization.
2Adaptability or versatility
If multiple types of metrics are collected from devices, then device classification capability is improved, but data processing requirements increase
Solution Approach 1:
The patent divides metrics into distinct categories (high-level and low-level) with specific examples for each. High-level metrics include packet counts and communication parameters, while low-level metrics include voltage, current, and waveform characteristics. This segmentation allows for targeted collection and processing of relevant data without overwhelming the system with unnecessary information.
Solution Approach 2:
The patent establishes threshold values for metrics in advance before actual device identification occurs. These pre-defined thresholds enable rapid comparison and classification during operation, eliminating the need for complex real-time analysis and improving processing efficiency.
3Reliability
If device metrics are continuously monitored and analyzed, then security against malicious behavior is improved, but computational resources are consumed
Solution Approach 1:
The patent pre-establishes threshold values for device metrics that define normal operational ranges. During continuous monitoring, the system only needs to compare current metrics against these pre-set thresholds rather than performing complex analysis, significantly reducing computational resource requirements while maintaining effective security monitoring.
Solution Approach 2:
The system continuously compares collected device metrics against established thresholds and device profiles, providing feedback that triggers alerts when anomalies are detected. This feedback mechanism enables continuous security monitoring using simple comparison operations rather than computationally intensive analysis.
Data Source
AI summary
Systems and methods for device identity augmentation. In some embodiments, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory including program instructions stored thereon that, upon execution by the processor, cause the IHS to: receive high-level metrics; receive low-level metrics; determine, using a plurality of sets of threshold values, that the high-level metrics and the low-level metrics match at least one of a plurality of device profiles; and at least one of: (a) identify a device as belonging to class of devices corresponding to the matching device profile, or (b) identify whether at least a subset of the high-level metrics or the low-level metrics are outside one or more of the sets of threshold values.