Device Fingerprinting for Spoof-Resistant Network Identification
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Solution Overview
Problem
Existing access control systems face challenges in reliably identifying and managing diverse electronic devices across networks due to heterogeneity, software updates, and malicious spoofing, which complicates the implementation of unified access policies and exposes systems to security risks.
Innovation Solution
A computer system employs a fingerprinting probe to determine device characteristics, comparing them with reference fingerprints to establish a similarity measure, allowing for robust device identification and policy enforcement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional access control methods using device identifiers are used, then access policies can be implemented, but devices can be spoofed or misidentified due to MAC address randomization and software updates
Solution Approach 1:
The patent segments the device identification process into multiple independent fingerprinting dimensions including hardware characteristics, software environment, network behavior patterns, and temporal features. Each dimension contributes a portion of the overall device fingerprint, making spoofing difficult as an attacker would need to replicate multiple independent characteristics simultaneously rather than a single identifier
Solution Approach 2:
The patent creates a composite device fingerprint by combining multiple heterogeneous features (hardware IDs, browser characteristics, canvas rendering patterns, audio context data, network stack behavior) into a unified identification profile. This composite approach resembles composite materials where the combination of different properties creates a more robust and难以复制 identification mechanism
2Measurement precision
If device identification methods are made more robust to handle heterogeneity and updates, then identification accuracy improves, but system complexity increases
Solution Approach 1:
The patent implements a universal fingerprinting framework that works across diverse device types, operating systems, and browser environments. The same core methodology collects and processes multiple feature dimensions regardless of the specific device platform, enabling accurate identification without requiring device-specific customization while maintaining high precision across heterogeneous environments
Solution Approach 2:
The patent dynamically adjusts the weight and selection of fingerprinting parameters based on device characteristics, network conditions, and observed behavior patterns. The system can adaptively change which features are most discriminative for different device types, optimizing identification accuracy while managing complexity through intelligent parameter selection rather than processing all possible features uniformly
3Adaptability or versatility
If fingerprinting probes collect multiple feature values to create comprehensive device fingerprints, then device distinction capability improves, but data processing requirements and computational overhead increase
Solution Approach 1:
The patent implements selective fingerprinting where not all feature dimensions are collected or processed with equal depth for every device. The system identifies and focuses on the most discriminative features for each device type or scenario, collecting sufficient information for accurate identification without the exhaustive overhead of gathering and processing every possible feature uniformly across all devices
Solution Approach 2:
The patent performs preliminary filtering and preprocessing of fingerprinting data at the point of collection, eliminating redundant or low-value features before they enter the main processing pipeline. By pre-processing and selecting only the most relevant feature values initially, the system reduces the computational burden on subsequent analysis stages while maintaining the ability to distinguish between devices
Data Source
AI summary
Some embodiments construct a device fingerprint of a target device (e.g., personal computer, smartphone) by intercepting an access request issued by the target device. A part of the device fingerprint may be determined according to the access request, e.g., according to various features of a TLS handshake, TCP header, and/or HTTP header of the respective access request. Another part of the device fingerprint is determined by transmitting a fingerprinting probe to the target device in response to the access request, and using executable code included in the fingerprinting probe to extract a set of features characterizing a browser executing on the target device. Some embodiments then compare the device fingerprint with reference fingerprints determined for other devices on the same network, to determine whether the target device is a familiar device or not. Exemplary applications include access control and personalized content distribution, among others.