Abstract
Cryptocurrencies have increasingly become the preferred choice for private transactions due to their anonymity and decentralized features. When a user creates transactions using wallet software with built-in Tor module, their identity information is further protected. At the same time, however, this combination of Tor and cryptocurrency is misused to carry out illegal acts, while the perpetrators are difficult to detect. Therefore, it is important to study traffic correlation methods for cryptocurrencies over Tor to maintain a healthy blockchain ecosystem. In this paper, based on existing work, we propose CryptoCorr, a traffic analysis model for cryptocurrency wallets, which can screening the collected Tor traffic data based on time window and flow features, and implement traffic correlation for cryptocurrency wallets based on deep learning architecture. We validate the proposed model by constructing a dataset with 82077 collected packets of wallet, and the experiment results demonstrate the effectiveness of the CryptoCorr model.
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This work is partially supported by National Key R &D Program of China under Grant 2020YFB1006101.
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Kong, X., Shen, M., Che, Z., Yu, C., Zhu, L. (2022). Traffic Correlation for Deanonymizing Cryptocurrency Wallet Through Tor. In: Svetinovic, D., Zhang, Y., Luo, X., Huang, X., Chen, X. (eds) Blockchain and Trustworthy Systems. BlockSys 2022. Communications in Computer and Information Science, vol 1679. Springer, Singapore. https://doi.org/10.1007/978-981-19-8043-5_21
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DOI: https://doi.org/10.1007/978-981-19-8043-5_21
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