Cryptocurrency Wallet Artifact Detection in File Systems
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for detecting cryptocurrency wallet artifacts on devices are manual, time-consuming, and lack comprehensive detection capabilities, especially for newly introduced wallets and cryptocurrency-related browsing activities, making it difficult for investigators to efficiently trace illegal financial transactions and recover stolen funds.
Innovation Solution
A system and method utilizing machine learning classifiers, neural networks, natural language processing, and string search algorithms to automatically detect cryptocurrency wallet artifacts, including application folders, images, and web browser data, on smart devices, enabling the classification of crypto-related applications, images, and browsing activities with high accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to detect cryptocurrency wallet artifacts, then investigators can examine files, but the process is time-consuming and lacks comprehensive detection capabilities
Solution Approach 1:
The patent replaces manual mechanical examination methods with automated machine learning-based detection systems. The ML classifier automatically scans file systems, identifies cryptocurrency wallet artifacts, and categorizes them without human intervention, thereby eliminating the time-consuming nature of manual inspection while maintaining comprehensive detection capabilities
Solution Approach 2:
The system enables self-service detection by automatically performing artifact identification and classification without requiring investigator input. The machine learning model independently processes file systems, detects cryptocurrency-related files, and generates results, allowing investigators to obtain comprehensive detection results without investing significant time in the process
2Measurement precision
If comprehensive detection capabilities are implemented to detect all types of crypto wallets including newly introduced ones, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The patent implements a universal detection system that can identify multiple types of cryptocurrency wallet artifacts through a single machine learning classifier. The system is designed to detect various wallet types including mobile wallets, desktop wallets, and newly introduced wallets by analyzing common artifact patterns across different wallet implementations, thereby achieving comprehensive detection without proportionally increasing system complexity
Solution Approach 2:
The system achieves high detection accuracy for diverse wallet types by dynamically adjusting detection parameters and using trained machine learning models that adapt to different artifact characteristics. The ML classifier processes various file patterns, extensions, and structures associated with different cryptocurrency wallets, maintaining high precision while managing complexity through parameter-based differentiation rather than separate detection mechanisms
3Productivity
If automated machine learning-based detection is used, then detection speed and accuracy improve, but computational resources and processing time increase
Solution Approach 1:
The patent applies partial action by implementing a two-stage detection process: first using a lightweight ML classifier for rapid initial screening of file systems to identify potential cryptocurrency artifacts, then applying more computationally intensive analysis only to suspected files. This approach achieves high detection efficiency while minimizing overall computational resource consumption by avoiding exhaustive analysis of entire file systems
Solution Approach 2:
The system segments the detection process into distinct phases: initial file system scanning, artifact identification, and detailed analysis. Each phase uses appropriately optimized algorithms, with the ML classifier handling the resource-intensive classification task only on relevant files rather than all files, thereby improving detection efficiency while controlling computational resource usage
Data Source
AI summary
Systems, methods, and frameworks for detecting cryptocurrency wallet artifacts in a file system of a device are provided. The cryptocurrency wallet artifacts can be automatically detected and can include: (i) cryptocurrency wallet application folders; (ii) images containing cryptocurrency artifacts (e.g., mnemonics phrases and/or transactions information); and/or (iii) web browsers artifacts (e.g., cache data, credentials, cookies, and/or bookmarks). This information can be analyzed and extracted using machine learning (ML), natural language processing (NLP), a convolution neural network (CNN), a recurrent neural network (RNN), and/or a string search algorithm.