Technology has become an integral part of contemporary society. The current transition from an industrial society to an information society is accompanied by the implementation of new technologies in every part of human activity. Increasing pressure to apply ICT in critical infrastructure resulted in the creation of new vulnerabilities. Traditional safety approaches are ineffective in a considerable number of cases. Therefore, machine learning another evolutionary step that provides robust solutions for extensive and sophisticated systems. The article focuses on cybersecurity research for industrial control systems that are widely used in the field of critical information infrastructure. Moreover, cybernetic protection for industrial control systems is one of the most important security types for a modern state. We present an adaptive solution for defense against cyber-attacks, which also consider the specifics of the industrial control systems environment. Moreover, the experiments are based on four machine learning algorithms (artificial neural network, recurrent neural network LSTM, isolation forest, and algorithm OCSVM). The proposed anomaly detection system utilizes multiple techniques and processes as preprocessing techniques, optimization techniques, and processes required for result interpretation. These procedures allow the creation of an adaptable and robust system that meets the need for industrial control systems.

科技已经成为当代社会不可或缺的一部分。当前从工业社会向信息社会的过渡伴随着新技术在人类活动的各个部分的实施。在关键基础设施中应用 ICT 的压力越来越大，导致新漏洞的产生。在相当多的情况下，传统的安全方法是无效的。因此，机器学习的另一个进化步骤为广泛而复杂的系统提供了强大的解决方案。文章重点关注广泛应用于关键信息基础设施领域的工业控制系统的网络安全研究。此外，工业控制系统的控制论保护是现代国家最重要的安全类型之一。我们提出了一种防御网络攻击的自适应解决方案，该解决方案还考虑了工业控制系统环境的具体情况。此外，实验基于四种机器学习算法（人工神经网络、循环神经网络 LSTM、隔离森林和算法 OCSVM）。所提出的异常检测系统利用多种技术和过程作为预处理技术、优化技术和结果解释所需的过程。这些程序允许创建满足工业控制系统需求的适应性强的系统。循环神经网络 LSTM、隔离森林和算法 OCSVM）。所提出的异常检测系统利用多种技术和过程作为预处理技术、优化技术和结果解释所需的过程。这些程序允许创建满足工业控制系统需求的适应性强的系统。循环神经网络 LSTM、隔离森林和算法 OCSVM）。所提出的异常检测系统利用多种技术和过程作为预处理技术、优化技术和结果解释所需的过程。这些程序允许创建满足工业控制系统需求的适应性强的系统。