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.

Measures to protect against cyberattacks are unable to provide a 100% guarantee that an attacker cannot penetrate an information system. If an attacker has gained access to the system, then such actions should be detected as soon as possible and interrupt access, as well as an investigation to fix security gaps. Methods used to detect attacks are divided into detecting misuse and detecting anomalies. This paper investigates the applicability of a frequency method that detects anomalies in the system by analyzing the entropy of the event log. This method is typically used to detect anomalies in network traffic, and unauthorized activities can also be indicated by anomalies in the hosts' event log. Studies on the Windows event log have shown that by analyzing the entropy, it is possible to detect exceeding the security thresholds by the number of different messages in the event log. This may indicate anomalies in the operation of the information system. The proposed method can be integrated into intrusion detection systems that notify the security administrator of possible violations.