Device Fingerprinting via Temporal Communication Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems and methods for identifying computing devices in a computer network are insufficient, as they only analyze Ethernet traffic or RF signals, leading to incorrect profiling and inadequate protection against external attacks.
Innovation Solution
The method involves continuously classifying temporal communication data by creating preprocessing models and training a neural network to derive properties from the data, which are then used to define and refine a device fingerprint.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only Ethernet traffic or RF signals are analyzed for device identification, then the identification process is simple, but the identification accuracy and reliability are insufficient
Solution Approach 1:
The patent combines multiple data sources (Ethernet traffic, RF signals, device responses) and multiple analysis techniques (temporal pattern analysis, machine learning models, fingerprinting) into a unified identification system. This merging of diverse inputs resolves the contradiction by achieving high identification accuracy through comprehensive data aggregation while managing complexity through integrated processing architecture.
Solution Approach 2:
The identification system creates a composite fingerprint profile that integrates characteristics from multiple data types and analysis methods. This composite approach combines temporal patterns, statistical features, and machine learning outputs to form a robust identification signature, achieving high precision while the modular composite structure helps manage system complexity.
2Measurement precision
If comprehensive temporal communication data is collected and analyzed using neural networks, then device identification accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing of temporal communication data by extracting temporal patterns and creating preprocessed feature sets before neural network analysis. This preliminary action prepares data in advance, reducing the computational burden during actual identification and minimizing processing time while maintaining high accuracy through thorough pre-analysis.
Solution Approach 2:
The complex data processing task is segmented into distinct stages: temporal pattern extraction, feature engineering, model training, and identification. This segmentation allows parallel processing of different data aspects and optimizes computational efficiency at each stage, reducing overall processing time while maintaining comprehensive analysis for high accuracy.
3Reliability
If continuous monitoring and refinement of device fingerprints is implemented, then security protection is enhanced, but system resource consumption increases
Solution Approach 1:
The system implements periodic refinement of device fingerprints through continuous monitoring of temporal communication patterns. By updating fingerprints at optimized intervals based on detected changes in device behavior, the system maintains high security reliability while avoiding unnecessary continuous processing that would waste resources. The periodic action adapts to device activity levels to balance security and resource consumption.
Data Source
AI summary
Systems and methods to generate a hyper context associated with a computing device are described. In one embodiment, communication data associated with the computing device is accessed. One or more features associated with the computing device are extracted from the communication data. A type of the computing device is detected. An operating system associated with the computing device is detected. A control associated with the computing device is detected. A functionality of the computing device is detected. An ownership of the computing device is detected. A hyper context associated with the computing device is defined. The hyper context includes a type context, a category context, an ownership context, a connectivity context, and a control context.