The paper addresses the problem of anomaly detection in network traffic and proposes a comprehensive solution to enhance the level of cybersecurity for organizations of various scales. A comparative analysis of existing monitoring and anomaly detection systems has been carried out, including both open-source solutions and commercial products.
The article explores approaches to forecasting the development trends of the IT market using machine learning methods. The relevance of the research is driven by the high dynamics of the digital economy, rapid technological changes, and the need for scientifically grounded analytical tools in the IT domain. The purpose of the study is to develop a forecasting model capable of identifying patterns in socio-economic, technological, and behavioral indicators that determine the state and prospects of IT market development.
In this article, the authors focused on analyzing the possibilities of using artificial intelligence models for effective detection and analysis of cybercrimes. A comprehensive method using artificial intelligence algorithms, such as Random Forest and Isolation Forest algorithms, is developed and described to detect ransomware, which is one of the main threats to information security management systems (ISMS) in the field of critical infrastructure.
Currently, machine learning algorithms (ML) are increasingly integrated into everyday life. There are many areas of modern life where classification methods are already used. Methods taking into account previous predictions and errors that are calculated as a result of data integration to obtain forecasts for obtaining the classification result are investigated. A general overview of classification methods is conducted. Experiments on machine learning algorithms for multimodal data are performed.