Neural Network Device Fingerprinting via Temporal Data Analysis
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Solution Overview
Problem
Current systems for identifying computing devices in a network are inadequate as they only analyze Ethernet traffic and RF signals, leading to incorrect profiling and insufficient protection against external attacks.
Innovation Solution
The method involves continuously classifying temporal communication data using preprocessing models and neural networks to derive device properties, creating a device fingerprint that is refined over time, and can reverse-predict MAC addresses and generate composite fingerprints for comprehensive device identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional Ethernet traffic and RF signal analysis methods are used 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 including Ethernet traffic data, wireless communication data, and device sensor data into a unified device fingerprint. This merging of multiple identification methods resolves the contradiction by achieving higher identification accuracy through comprehensive data analysis while managing system complexity through integrated processing architecture.
Solution Approach 2:
The patent creates a composite device fingerprint that integrates characteristics from different data types and sources. This composite identification approach类似于composite materials principle, combining multiple properties (traffic patterns, RF characteristics, sensor readings) to achieve superior identification reliability that cannot be obtained by single-method analysis.
2Reliability
If comprehensive temporal communication data is continuously analyzed to create refined device fingerprints, then device identification accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary processing of communication data to extract relevant features and create initial device fingerprints before full analysis. This preliminary action prepares data in advance, allowing faster subsequent processing while maintaining high identification reliability through continuous refinement of device profiles over time.
Solution Approach 2:
The patent implements continuous device fingerprint refinement by continuously analyzing new temporal communication data and updating device profiles. This continuous process improves reliability over time while optimizing processing efficiency by building upon previously extracted features rather than reprocessing all raw data from scratch.
3Measurement precision
If device fingerprints are continuously refined using additional temporal communication data, then identification precision improves, but system complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where device fingerprints are continuously refined based on new temporal communication data. The system compares new data against existing fingerprints, updates profiles when discrepancies are found, and uses this feedback loop to progressively improve fingerprint precision while managing complexity through iterative optimization.
Solution Approach 2:
The patent creates dynamic device fingerprints that adapt and evolve over time as new communication data is analyzed. Rather than static identification profiles, the system continuously updates device characteristics, allowing the fingerprinting mechanism to become more precise over time while adapting to changing device behaviors and patterns.
Data Source
AI summary
Systems and methods to reverse-predict a MAC address associated with a computing device are described. In one embodiment, first temporal communication data associated with the computing device is accessed for a first time interval. The first temporal communication data is converted into a first image. Second temporal communication data associated with the computing device is accessed for a second time interval. The second temporal communication data is converted into a second image. An image ensemble including the first image and the second image is analyzed using a neural network. Each image in the image ensemble is converted from temporal communication data associated with the computing device. The neural network learns a temporal pattern associated with the image ensemble. Current temporal communication data associated with the computing device is accessed and converted into a current image. The current image is compared with the temporal pattern. A MAC address associated with the computing device is reverse-predicted responsive to the comparison.