Analyzing In-browser Cryptojacking

TL;DR

This paper provides a comprehensive analysis of in-browser cryptojacking, including static, dynamic, and economic aspects, and proposes countermeasures to mitigate such attacks.

Contribution

It introduces a systematic approach combining static, dynamic, and economic analysis of cryptojacking, and develops machine learning models with high accuracy for detection.

Findings

Cryptojacking scripts can be distinguished from benign scripts with 100% accuracy.

Cryptojacking has a significant impact on CPU and battery resources.

Economically, cryptojacking is shown to be infeasible as an alternative to online advertising.

Abstract

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. To understand and counter such attacks, we systematically analyze the static, dynamic, and economic aspects of in-browser cryptojacking. For static analysis, we perform content, currency, 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.