Mind your wallet's privacy: Identifying bitcoin wallet apps and user's actions through network traffic analysis
Proceedings of the ACM Symposium on Applied Computing, Vol: Part F147772, Page: 1484-1491
2019
- 23Citations
- 44Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
With the surge in popularity of cryptocurrencies, Bitcoin has emerged as one of the most promising means for remittance, payments, and trading. Supplemented by the convenience offered by the smartphones, an increasing number of users are adopting Bitcoin wallet apps for different purposes. In this paper, we focus on identifying user activities on smartphone-based Bitcoin wallet apps that are commonly used for sending, receiving, and trading Bitcoin. To accomplish our goal, we performed network traffic analysis using machine learning techniques. Since we focus on apps of the same type/functionality, it makes our classification problem even more difficult compared to classifying apps tailored for discrete purposes. Moreover, our goal is to identify user activities even in the presence of encryption. In our experiments, we considered the worldwide most downloaded Bitcoin wallet apps on both Google Play Store and Apple's App Store. For collecting network traffic traces, we used only physical hardware and omitted any emulator to build our experiment scenario as close to the real environment as possible. We process the traffic traces in several phases before extracting the features that are utilized to train our supervised learning algorithms. We deal with the classification problem in multiple stages in a hierarchical fashion. We ran a thorough set of experiments to assess the performance of our system and attained nearly 95% accuracy in user activity identification.
Bibliographic Details
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