Methods and models for emulating negative impacts on information system resources

Modern information systems and existing protection methods often cannot keep up with the rapid evolution of threats, and traditional approaches to security testing do not always allow for the identification of all potential vulnerabilities. Methods of emulating negative impacts on information system resources are in a stage of active development and are used in various fields, but they require further improvement and systematization. The aim of the article is to uncover and analyze modern methods and models of emulating negative impacts in a broader context, as well as to determine their advantages, limitations, and areas of application. The study examines the emulation of DoS/DDoS attacks using traffic models, such as the Poisson model and fractal models, as well as modeling botnets based on epidemiological models. Methods of fault injection and chaos engineering, along with machine learning and artificial intelligence techniques, have been investigated as tools for testing system reliability and fault tolerance. The conclusion of this study is that the application of mathematical models such as Poisson processes, fractal models, epidemiological models, and Monte Carlo methods allows for more accurate modeling of negative impacts and assessment of system resilience to various types of attacks and failures. A comprehensive approach to emulating negative impacts, which combines different methods and models, is necessary for enhancing cybersecurity levels. This approach allows not only for the detection of existing vulnerabilities, but also for proactive responses to potential threats. The detection and neutralization of these attacks is a complex task that requires a comprehensive approach integrating various modeling, analysis, and counteraction methods.