IoT Device Type Detection via Behavioral Profile Similarity
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Solution Overview
Problem
In environments with Internet of Things (IoT) devices, there is a need to identify unknown device types and associate them with functional groups, as existing methods lack effective mechanisms for determining device types based on behavior patterns.
Innovation Solution
A system and method that collect and compare statistical data from unknown IoT devices against behavior profiles of known devices, using a similarity analysis to assign device types by generating a similarity score that meets or exceeds a predetermined threshold, thereby determining the device type associated with a functional group.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If behavioral data collection and comparison methods are implemented to identify unknown IoT device types, then device type identification accuracy is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments the device identification process into distinct modules: data collection from multiple sources (device banners, network traffic, behavioral patterns), behavior profiling for known devices, similarity computation, and threshold-based classification. This segmentation allows each module to be optimized independently while maintaining overall system accuracy.
Solution Approach 2:
The patent introduces behavioral profiles as an intermediary layer between raw device data and device type classification. These profiles serve as reference models that mediate the comparison process, enabling accurate identification of unknown devices by matching their behavioral patterns against established profiles without requiring direct device-to-device comparison.
2Measurement precision
If comprehensive behavioral data is collected and analyzed, then device type determination accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-collecting and storing behavioral profiles for known device types before encountering unknown devices. These profiles include pre-computed behavioral characteristics and patterns that can be quickly referenced during identification, eliminating the need for real-time analysis of all possible device behaviors.
Solution Approach 2:
The patent transforms raw behavioral data into standardized parameters and metrics that facilitate efficient comparison. By converting diverse device behaviors into uniform statistical measures and similarity scores, the system enables rapid processing while maintaining comprehensive analysis capabilities.
3Adaptability or versatility
If behavior profiles are created and maintained for functional groups, then unknown device classification capability is improved, but storage requirements and database management complexity increase
Solution Approach 1:
The system creates universal behavioral profiles that can represent entire functional groups of devices rather than individual devices. Each profile encapsulates the common behavioral characteristics of a group, enabling a single profile to serve multiple classification purposes and reducing the total number of profiles required in the database.
Solution Approach 2:
The patent merges behavioral data from multiple devices of the same functional type into a single consolidated profile. This combining process aggregates similar behavioral patterns while filtering out device-specific variations, reducing storage requirements while maintaining the ability to accurately classify unknown devices belonging to that functional group.
Data Source
AI summary
In order to identify an unknown IoT device type, behavioral or statistical data of the device is collected and analyzed. A functional group may be created using behavioral data of devices of a known type. A behavior profile for the functional group may be generated and stored in a database. The behavioral data of the device of an unknown type is compared to the behavior profile of the functional group. When the similarity of the behavioral data of the device of an unknown type and the behavior profile exceeds a predetermined or configurable threshold, a device type associated with the functional group can be assigned to the device of a previously unknown type.