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Analyzing In-Browser Cryptojacking
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AI
DOI：10.1109/TDSC.2024.3377533.png)
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摘要
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Cryptojacking is the permissionless use of a target device to covertly mine cryptocurrencies. With cryptojacking, attackers use malicious JavaScript codes to force web browsers into solving proof-of-work puzzles, thus making money by exploiting the resources of the website visitors. We systematically analyze the static, dynamic, and economic aspects of in-browser cryptojacking to understand and counter such attacks. For static analysis, we perform currency-based and code-based categorization of cryptojacking samples to 1) measure their distribution across websites, 2) highlight their platform affinities, and 3) study their code complexities. We apply machine learning techniques to distinguish cryptojacking scripts from benign and malicious JavaScript samples with 100% accuracy. For dynamic analysis, we analyze the effect of cryptojacking on critical system resources, such as CPU and battery usage. We also perform web browser fingerprinting to analyze the information exchange between the victim node and the dropzone cryptojacking server. We also build an analytical model to empirically evaluate the feasibility of cryptojacking as an alternative to online advertisement. Our results show a sizeable negative profit and loss gap, indicating that the model is economically infeasible. Finally, leveraging insights from our analyses, we build countermeasures for in-browser cryptojacking that improve the existing remedies.
Keyword:
Codes
Internet
Bitcoin
Servers
Peer-to-peer computing
Market research
Browsers
Coinhive
cryptojacking
illegal mining
期刊
IF:
7.5
论文数:
2.6K
被引数:
9.6K
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