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

Main Article Content

Aaron Zimba
Christabel Ngongola-Reinke
Mumbi Chishimba
Tozgani Fainess Mbale

Abstract

Cryptocurrencies have emerged as a new form of digital money that has not escaped the eyes of cyber-attackers. Traditionally, they have been maliciously used as a medium of exchange for proceeds of crime in the cyber dark-market by cyber-criminals. However, cyber-criminals have devised an exploitative technique of directly acquiring cryptocurrencies from benign users' CPUs without their knowledge through a process called crypto mining. The presence of crypto mining activities in a network is often an indicator of compromise of illegal usage of network resources for crypto mining purposes. Crypto mining has had a financial toll on victims such as corporate networks and individual home users. This paper addresses the detection of crypto mining attacks in a generic network environment using dynamic network characteristics. It tackles an in-depth overview of crypto mining operational details and proposes a semi-supervised machine learning approach to detection using various crypto mining features derived from complex network characteristics. The results demonstrate that the integration of semi-supervised learning with complex network theory modeling is effective at detecting crypto mining activities in a network environment. Such an approach is helpful during security mitigation by network

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How to Cite
Zimba, A., Ngongola-Reinke, C., Chishimba, M., & Mbale, T. (2021). Demystifying Cryptocurrency Mining Attacks: A Semi-supervised Learning Approach Based on Digital Forensics and Dynamic Network Characteristics. Zambia ICT Journal, 5(1), 1-7. Retrieved from https://ictjournal.icict.org.zm/index.php/zictjournal/article/view/108
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Articles
Author Biographies

Aaron Zimba, Mulungushi University

Department of Computer Science & IT

Christabel Ngongola-Reinke, Mulungushi University

Department of Economics

Mumbi Chishimba, Zambia Revenue Authority

Department of Information Communication Technology

Tozgani Fainess Mbale, University of Zambia

Department of Electrical & Electronics