Emulator Detection via User Agent and Device Model Consistency
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Solution Overview
Problem
Conventional methods for detecting emulators used in fraudulent electronic transactions are ineffective due to the ability of advanced emulators to mimic device parameters, making it difficult to distinguish between real and simulated devices.
Innovation Solution
A system and method that transform hardware configurations into device model representations and analyze the consistency with user agent representations to detect emulators, using user agent sentence embedding and machine learning algorithms to generate vector representations for accurate identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If configuration-based indicators (SIM configurations, hardware configurations, hardware identification numbers) are used to detect emulators, then detection capability is improved, but the effectiveness deteriorates as emulators improve their ability to imitate device parameters
Solution Approach 1:
The patent introduces an intermediary approach by analyzing the relationship between user agent strings and device model predictions. Instead of directly detecting emulator configurations, the system uses user agent strings as intermediaries that contain semantic information about device characteristics, and compares these with predicted device models to identify inconsistencies indicating emulator usage.
Solution Approach 2:
The patent changes the detection parameters from direct hardware configuration indicators to user agent string semantics. By transforming user agent strings into vector representations and comparing them with device model predictions, the system uses semantic parameter changes rather than fixed configuration checks, making detection more robust against emulator parameter imitation.
2Adaptability or versatility
If emulators imitate all device parameters to avoid detection, then their ability to conduct fraud is improved, but the difficulty of detecting them increases
Solution Approach 1:
The patent applies a form of copying by creating a virtual device model representation that copies essential device characteristics from the user agent string. This virtual model is then compared with the actual device behavior to detect inconsistencies, allowing detection without requiring direct observation of emulator configurations.
Solution Approach 2:
The patent substitutes the mechanical approach of checking hardware configurations with a semantic analysis approach. Instead of mechanically verifying hardware parameters, the system uses natural language processing to analyze user agent string semantics and compares them with device model predictions, replacing configuration-based detection with semantic consistency checking.
Data Source
AI summary
Systems and methods for emulator detection are disclosed. In one embodiment, a user agent string may be embedded into a first numerical data vector representation. Hardware characteristics of a client device corresponding to the user agent may be embedded into a second numerical data vector representation. Based on the first numerical data vector representation of the user agent and the second numerical data vector representation of the hardware characteristics, and their consistency, the client device may be determined to be an emulator or a non-emulator device.