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The article examines the features of risk assessment for automated vehicles operating in a mixed traffic environment in which automated and conventional road users interact simultaneously. The relevance of the topic is determined by the high level of road accidents attributable to the human factor, as well as by the increasing complexity of risk structures arising from the implementation of intelligent transport systems.

The aim of the study is to develop a nonlinear synergistic model for integrated risk assessment of automated vehicles operating in a mixed traffic environment.

This article is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)

In the modern world, software security has become a top priority, as it directly determines the reliability of digital solutions and user trust. The growing number of cyber threats and the increasing complexity of software systems highlight the necessity of using effective tools for control and vulnerability prevention.

This article is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)

This article is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)

The key elements of the Industry 4.0 Concept and Ukraine’s Cybersecurity Strategy call for the development of methodological approaches to the security of intelligent cyber-physical medical technologies to provide functional support for secure digitalization in Ukraine’s healthcare sector.

This article is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)

This article is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)

This work presents statistical anomaly detectionmethods for cyber-physical systems using the Secure Water Treatment dataset, a scaled down version of a real-world industrial water treatment plant. We consider a critical sensor signal, the water level indicator LIT301, and set up two corresponding models for detecting cyber-attacks - a sliding window Z-score outlier detection, and an autoregressive integrated moving average time-series forecasting model of order (3,0,5).

The cybersecurity implications of numerical anomalies inherent to the IEEE 754 floating-point arithmetic standard remain insufficiently studied, despite their confirmed role in critical software vulnerabilities across embedded systems, cryptographic libraries, and web applications. The special-value semantics of IEEE 754-2019 – including Not-a-Number (NaN), signed zero, positive and negative infinity, and denormalized (subnormal) numbers – were investigated as potential attack vectors and vulnerability amplifiers in real-world computing systems.

The article examines the administrative and legal framework for the digitalisation of public administration in Ukraine’s agricultural sector as a comprehensive mechanism com-bining regulatory norms, institutional capacity, and organisational and procedural instru-ments for implementing digital solutions in the activities of public authorities.

The research introduces a hybrid behavioural analysis technique for early detection of anomalous user behavior observed on web applications. This strategy involves statistical probability modeling and sequence- based deep learning to design interpretable and robust anomaly detection. A probability baseline has been obtained as a probabilistic basis using KDE (Kernel Density Estimation) and longitudinal time series patterns are sampled using an LSTM network. The hybrid anomaly score combines these two models to dynamically analyze behavioural deviations.