In recent years, there has been a noticeable increase in cyberattacks using ransomware. Attackers use this malicious software to break into networks and harm computer systems. This has caused significant and lasting damage to various organizations, including government, private companies, and regular users. These attacks often lead to the loss or exposure of sensitive information, disruptions in normal operations, and persistent vulnerabilities. This paper focuses on a method for recognizing and identifying ransomware in computer networks. The approach relies on using machine learning algorithms and analyzing the patterns of network traffic. By collecting and studying this traffic, and then applying machine learning models, we can accurately identify and detect ransomware. The results of implementing this method show that machine learning algorithms can effectively pinpoint ransomware based on network traffic, achieving high levels of precision and accuracy.

翻译：近年来，利用勒索软件的网络攻击显著增加。攻击者使用这种恶意软件入侵网络并破坏计算机系统，给政府机构、私营企业和普通用户等各类组织造成了严重且持久的损害。此类攻击常导致敏感信息泄露、正常运营中断以及持续性漏洞问题。本文聚焦于一种识别和检测计算机网络中勒索软件的方法。该方法依赖机器学习算法与网络流量模式分析。通过收集并研究网络流量数据，再应用机器学习模型，能够准确识别并检测勒索软件。实际应用结果表明，基于网络流量的机器学习算法可有效定位勒索软件，并达到较高的精确度与准确率。