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Cryptocurrencies have emerged as a new form of digital money that has notescaped the eyes of cyber-attackers. Traditionally, they have been maliciouslyused as a medium of exchange for proceeds of crime in the cyber dark-market bycyber-criminals. However, cyber-criminals have devised an exploitativetechnique of directly acquiring cryptocurrencies from benign users' CPUswithout their knowledge through a process called crypto mining. The presence ofcrypto mining activities in a network is often an indicator of compromise ofillegal usage of network resources for crypto mining purposes. Crypto mininghas had a financial toll on victims such as corporate networks and individualhome users. This paper addresses the detection of crypto mining attacks in ageneric network environment using dynamic network characteristics. It tacklesan in-depth overview of crypto mining operational details and proposes asemi-supervised machine learning approach to detection using various cryptomining features derived from complex network characteristics. The resultsdemonstrate that the integration of semi-supervised learning with complexnetwork theory modeling is effective at detecting crypto mining activities in anetwork environment. Such an approach is helpful during security mitigation bynetwork security administrators and law enforcement agencies.

Demystifying Cryptocurrency Mining Attacks: A Semi-supervised Learning Approach Based on Digital Forensics and Dynamic Network Characteristics