期刊
SAC '19: PROCEEDINGS OF THE 34TH ACM/SIGAPP SYMPOSIUM ON APPLIED COMPUTING
卷 -, 期 -, 页码 1484-1491出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3297280.3297430
关键词
Android; Bitcoin; iOS; Machine learning; Traffic analysis
资金
- Fondazione Cassa di Risparmio di Padova e Rovigo (CARIPARO)
- EU TagItSmart! Project (H2020-ICT30-2015) [688061]
- EU-India REACH Project [ICI+/2014/342-896]
- Cisco University Research Program Fund [2017-166478 (3696)]
- Silicon Valley Community Foundation
- grant Scalable IoT Management and Key security aspects in 5G systems from Intel
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.
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