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Cryptojacking is a type of computer piracy in which a hacker uses a victim’s computer resources, without their knowledge or consent, to mine for cryptocurrency. This is made possible by new memory-based cryptomining techniques and the growth of new web technologies such as WebAssembly, allowing mining to occur within a browser. Most of the research in the field of cryptojacking has focused on detection methods rather than prevention methods. Some of the detection methods proposed in the literature include using static and dynamic features of in-browser cryptojacking malware, along with machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), and others. However, these methods can be effective in detecting known cryptojacking malware, but they may not be able to detect new or unknown variants. The existing prevention methods are shown to be effective only against web-assembly (WASM)-based cryptojacking malware and cannot handle mining service-providing scripts that use non-WASM modules. This paper proposes a novel hybrid approach for detecting and preventing web-based cryptojacking. The proposed approach performs the real-time detection and prevention of in-browser cryptojacking malware, using the blacklisting technique and statistical code analysis to identify unique features of non-WASM cryptojacking malware. The experimental results show positive performances in the ease of use and efficiency, with the detection accuracy improved from 97% to 99.6%. Moreover, the time required to prevent already known malware in real time can be decreased by 99.8%.

Digital agriculture has rapidly developed in the last decade in many countries where the share of agricultural production is a significant part of the total volume of gross production. Digital agroecosystems are developed using a variety of IT solutions, software and hardware tools, wired and wireless data transmission technologies, open source code, Open API, etc. A special place in agroecosystems is occupied by electronic payment technologies and blockchain technologies, which allow farmers and other agricultural enterprises to conduct commodity and monetary transactions with suppliers, creditors, and buyers of products. Such ecosystems contribute to the sustainable development of agriculture, agricultural engineering, and management of production and financial operations in the agricultural industry and related industries, as well as in other sectors of the economy of a number of countries. The introduction of crypto solutions in the agricultural sector is designed to create integrated platforms aimed at helping farmers manage supply lines or gain access to financial services. At the same time, there are risks of illegal use of computing power for cryptocurrency mining—cryptojacking. This article offers a thorough risk assessment of cryptojacking attacks on endpoint systems, focusing on identifying critical vulnerabilities within IT infrastructures and outlining practical preventive measures. The analysis examines key attack vectors—including compromised websites, infected applications, and supply chain infiltration—and explores how unauthorized cryptocurrency mining degrades system performance and endangers data security. The research methodology combines an evaluation of current cybersecurity trends, a review of specialized literature, and a controlled experiment simulating cryptojacking attacks. The findings highlight the importance of multi-layered protection mechanisms and ongoing system monitoring to detect malicious activities at an early stage.