  Emerging blockchain and cryptocurrency-based technologies are redefining the
way we conduct business in cyberspace. Today, a myriad of blockchain and
cryptocurrency systems, applications, and technologies are widely available to
companies, end-users, and even malicious actors who want to exploit the
computational resources of regular users through \textit{cryptojacking}
malware. Especially with ready-to-use mining scripts easily provided by service
providers (e.g., Coinhive) and untraceable cryptocurrencies (e.g., Monero),
cryptojacking malware has become an indispensable tool for attackers. Indeed,
the banking industry, major commercial websites, government and military
servers (e.g., US Dept. of Defense), online video sharing platforms (e.g.,
Youtube), gaming platforms (e.g., Nintendo), critical infrastructure resources
(e.g., routers), and even recently widely popular remote video
conferencing/meeting programs (e.g., Zoom during the Covid-19 pandemic) have
all been the victims of powerful cryptojacking malware campaigns. Nonetheless,
existing detection methods such as browser extensions that protect users with
blacklist methods or antivirus programs with different analysis methods can
only provide a partial panacea to this emerging cryptojacking issue as the
attackers can easily bypass them by using obfuscation techniques or changing
their domains or scripts frequently. Therefore, many studies in the literature
proposed cryptojacking malware detection methods using various
dynamic/behavioral features.
