Network Device Identification via Traffic Pattern Analysis
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Solution Overview
Problem
The challenge in managing local networks is the inability to accurately identify and track devices connected to them due to the flexible and intermittent nature of wireless connections, which obscures unique device identifiers and complicates security, network traffic, and bandwidth management.
Innovation Solution
A machine learning-based system that passively analyzes network traffic to identify devices by training models on communication patterns and attributes, even without unique device identifiers, using techniques like deep packet inspection and natural language processing to generate device profiles and associate them with identifiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If wireless connection technologies are integrated into devices to enable convenient network connectivity, then device usability and convenience are improved, but network device identification and tracking become difficult due to obscured identifiers and intermittent connections
Solution Approach 1:
The patent introduces network traffic analysis as an intermediary method to indirectly identify devices. Instead of relying on direct device identifiers that are obscured by wireless routing, the system analyzes communication patterns, packet metadata, and network behavior as mediators to infer device identity and characteristics
Solution Approach 2:
The patent replaces traditional mechanical/physical identification methods (MAC address reading, direct device enumeration) with information-based identification using machine learning models that process network traffic data to recognize device fingerprints and communication patterns
2Adaptability or versatility
If flexible wireless connections are allowed to connect devices to networks, then adaptability and versatility are improved, but security management and network traffic control become challenging
Solution Approach 1:
The system continuously monitors network traffic and uses machine learning models to identify devices in real-time, providing feedback to network administrators about connected devices, their locations, and their communication patterns, enabling dynamic security responses to identified devices
Solution Approach 2:
The patent performs preliminary device identification and profiling before security incidents occur by continuously analyzing network traffic patterns to establish baseline device behaviors, enabling proactive detection of anomalies and unauthorized devices
3Measurement precision
If deep packet inspection and machine learning analysis are applied to identify devices, then device tracking accuracy is improved, but system complexity and processing requirements increase
Solution Approach 1:
The patent extracts only the necessary information from network packets for device identification, such as packet metadata, communication patterns, and protocol usage, rather than analyzing all packet contents, thereby reducing processing complexity while maintaining identification accuracy
Solution Approach 2:
The system changes the parameters of network traffic analysis by transforming raw packet data into feature vectors and patterns that machine learning models can process, converting complex network data into simplified representations that balance accuracy with computational efficiency
Data Source
AI summary
Machine learning techniques are described for analyzing information network traffic to identify different devices connected to a network. Transmitted network packets may be passively collected and analyzed. In some cases the described techniques may be used to identify distinct devices connected to a network even though the collected and analyzed packets may lack a unique device identifier, such as a media access control (MAC) identifier, corresponding to a device that originated the packets.