Device Fingerprinting via Semantic Clustering
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Solution Overview
Problem
Conventional device fingerprinting methods are prone to errors, expensive to maintain, and have limited coverage and granularity, leading to misclassification of devices in complex network environments, particularly with the increasing number of IoT devices.
Innovation Solution
The implementation of similarity-based semantic clustering techniques to automatically identify and generate fingerprinting rules, allowing for accurate and granular classification of devices without manual intervention, by comparing device attributes and forming clusters based on similarity scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual rule creation and maintenance is used for device fingerprinting, then classification can be performed, but the process becomes expensive, error-prone, and cumbersome
Solution Approach 1:
The system performs self-service by automatically generating fingerprinting rules through clustering algorithms. The network monitor entity autonomously analyzes device attributes, forms clusters based on similarity, and generates classification rules without human intervention, eliminating the need for manual rule creation and maintenance while improving accuracy
Solution Approach 2:
The patent replaces the mechanical manual process of rule creation with an automated computational system. Clustering algorithms and similarity score calculations substitute for human analysts, transforming the manual mechanical process into an automated information processing system that reduces errors and complexity
2Adaptability or versatility
If conventional fingerprinting methods are used, then device classification is possible, but coverage and granularity are limited
Solution Approach 1:
The system dynamically adapts to the heterogeneous IoT landscape by using similarity-based clustering that automatically adjusts to new device types and attributes. The clustering approach is flexible and can handle varying levels of granularity, allowing the system to adapt its classification depth based on the data distribution and device diversity encountered
Solution Approach 2:
The patent adds the dimension of similarity scoring to traditional fingerprinting. By introducing similarity thresholds and multi-attribute clustering, the system creates additional classification dimensions that enhance both coverage and granularity, allowing devices to be classified at multiple levels of detail simultaneously
3Reliability
If manual fingerprinting rules are created, then classification can be performed, but extensive time and resources are required for creation and maintenance
Solution Approach 1:
The system performs preliminary action by automatically generating classification rules from observed device behavior patterns. The clustering process proactively creates fingerprinting rules before they are needed for classification, eliminating the time-consuming manual rule creation process while maintaining high accuracy through data-driven rule generation
Data Source
AI summary
Systems, methods, and related technologies for classification are described. Entity attribute data associated with network entities is obtained. One or more entity attributes for classifying a set of entities is determined based on the entity attribute data. A set of entities coupled to a network are monitored. Values of the one or more entity attributes for the plurality of entities is identified. The set of entities are clustered into one or more entity clusters based on a similarity of the one or more entity attributes for the entities. An entity fingerprinting action is then performed based on the entity clusters.