Accelerating Multilingual Cryptocurrency Forensics: An NLP-Driven Approach for Efficient Mnemonic Identification

The increasing use of cryptocurrencies in criminal activities presents significant challenges to society and the judicial system, particularly in tracking and seizing illicit digital assets. Among all relevant digital evidence, mnemonic phrases, which are critical for accessing cryptocurrency wallets, are crucial digital evidence for confiscating criminal proceeds and conducting investigations. However, traditional digital forensics tools, such as the Mnemonic Library Matching Method, lack flexibility and efficiency when handling cryptocurrency-related data. This study introduces an innovative Natural Language Processing (NLP) and deep learning approach for rapid mnemonic identification across 11 languages, including English, Spanish, and Japanese. We trained and compared four NLP deep learning models: RNN, LSTM, BiLSTM, and TextCNN, on a large-scale, real-world dataset. Our analysis reveals that the Text Convolutional Neural Network (TextCNN) model exhibits superior performance, achieving a 99.9993% accuracy rate, nearly matching the 100% accuracy of the Mnemonic Library Matching Method. Crucially, our TextCNN-driven approach processes data 40.47 times faster than the traditional method, significantly enhancing efficiency in time-sensitive forensic environments. This NLP-driven method not only maintains high accuracy while dramatically reducing processing time but also offers greater adaptability for diverse forensic needs compared to traditional techniques. By enabling more effective tracking and seizure of criminal assets, this approach aims to address the broader societal and judicial challenges posed by cryptocurrency-related criminal activities. Our research showcases the potential of NLP and deep learning in digital forensics, providing law enforcement with advanced tools for investigating cryptocurrency-related crimes and curbing the misuse of cryptocurrencies in illicit activities.