This paper addresses the problem of improving the robustness of image steganographic methods under lossy compression while preserving high perceptual quality and low computational complexity. The paper proposes an adaptive code-controlled steganographic method that enables spectrally selective embedding in the spatial domain through structured codewords. The proposed approach introduces block-level adaptivity in which the energy of the embedding codeword is dynamically selected according to the robustness characteristics of each image block. Instead of applying uniform embedding strength, the method determines the minimal codeword energy required to guarantee reliable message extraction under a predefined worst-case JPEG compression level. Experimental evaluation demonstrates that the proposed adaptive strategy significantly improves robustness to compression attacks while preserving high perceptual reliability and strong resistance to statistical steganalysis techniques. In particular, for JPEG quality factor (QF) = 50, the bit error rate is reduced to 1.25% while a high perceptual quality of 52.07 dB peak signal-to-noise ratio (PSNR) is achieved. For stronger attack conditions, QF = 20, the method achieves 6.6% bit errors with a PSNR of 47.7 dB. Overall, the proposed adaptive energy selection provides up to 22.68% fewer errors or up to 6.05 dB higher PSNR compared to the classical code-controlled steganographic method, confirming its effectiveness for practical steganographic applications.

In the context of the active and uncontrolled dissemination of information in the digital environment, especially on social media and news aggregators, the task of automatically identifying fake news has become especially relevant. The growing volume of user-generated content and the high speed of its distribution significantly complicate manual information verification, necessitating the use of machine learning methods. The aim of this study is to analyze, comparatively evaluate, and optimize baseline machine learning models for fake news detection, focusing on hyperparameter selection, computational efficiency, and interpretation of classification results. This study utilizes the ISOT Fake News Dataset, a text dataset containing Englishlanguage news items with binary labels labeled «true» and «false», cleaned and vectorized using the TF-IDF method. The study implemented and analyzed logistic regression, decision tree, random forest, and gradient boosting models. Classification quality was assessed using the following metrics: Accuracy, Precision, Recall, F1-score, as well as ROC analysis and the area under the curve (AUC). It was shown that the gradient boosting model provides the highest classification accuracy, while logistic regression demonstrates comparable quality with significantly lower computational costs and training time. Additionally, the study included data leakage monitoring and an analysis of the causes of inflated quality metrics, ensuring the correct interpretation of the results. The findings confirm that optimized classical machine learning models are capable of providing highquality fake news detection and can be considered a resource-efficient alternative to more complex neural network approaches in applied cybersecurity and information flow monitoring.

Railways remain an essential part of modern transportation, yet their safe functioning is often determined by the actual condition of the tracks. The study looks at various ways to detect faults in the rail infrastructure and splits them broadly into two categories: static and dynamic techniques. Different countries rely on different tools to monitor tracks, and this paper compares those tools based on practical factors like how precise they are, how much ground they cover, and how difficult or costly they are to operate. Rather than simply listing pros and cons, we try to show where each method works best. To make sense of the data, visuals like charts and summaries were added, making it easier to see where each approach fits. One part of the analysis pays special attention to how certain features of the railway - such as how wide the rails are or how much the outer rail is elevated - can influence the choice of inspection methods. Lately, there's been a shift toward smarter diagnostics. Technologies like AI, digital simulations of tracks (known as digital twins), and systems using many sensors at once are gaining ground. These tools are changing how track inspections are done and offer new opportunities for early problem detection. This paper doesn't just list methods - it gives a clear structure for understanding which approach fits what context. The outcomes can help transportation teams fine-tune how they take care of tracks and make the system more dependable in the long term.

The article is dedicated to assessing the impact of digitalization on educational practices and the professional activities of teachers. The aim of the study was to analyze primary data collected from the teaching community to identify the current level of digital competencies, the nature of the use of digital tools and artificial intelligence (AI) technologies, as well as their influence on the educational process and student outcomes. The research methodology included a quantitative and qualitative survey of 832 teachers from various educational institutions, conducted through an online questionnaire. The data collection covered demographic characteristics of the respondents, the frequency of use of digital tools (MS Office, Kahoot) and AI (ChatGPT, Gemini), the use of digital technologies in the classroom (interactive testing, AR), subjective assessment of their impact on student motivation and performance, as well as the need for professional development. The results of the study revealed active integration of digital tools, with MS Office being the most commonly used and growing popularity of AI tools. Teachers noted a significant increase in student engagement (84.6%) and improvement in content comprehension due to digitalization. However, despite the overall positive trend, 4.4% of respondents indicated negative aspects, such as decreased comprehension or increased time costs. A high level of interest in mastering interactive tasks, AR technologies, and AI wasidentified among educators, confirming the demand for continued professional development. The obtained data provide an empirical basis for the development of targeted training programs and the optimization ofstrategies for integrating digital technologies into the educational environment.

This article presents a comparative analysis of approaches to auditing Information Security Management Systems (ISMS) used in the Republic of Kazakhstan and other countries. The study examines key audit methodologies, including international regulatory frameworks such as ISO/IEC 27001 (International Organization for Standardization / International Electrotechnical Commission), NIST (National Institute of Standards and Technology), and the GDPR (General Data Protection Regulation), with a particular focus on their adaptation across different legal jurisdictions. Special attention is given to the strengths and limitations of various auditing practices, as well as the maturity levels of ISMS executions across different nations. The paper analyzes the current state of information security in Kazakhstan, taking into account the national regulatory landscape and the practical application of audit mechanisms in both public and private sectors. It also identifies critical challenges faced by organizations, such as the shortage of qualified personnel, difficulties in implementing contemporary standards and technologies, and weak interdepartmental coordination. Prospective directions for enhancing ISMS audit methods are outlined based on an evaluation of global best practices. Additionally, the paper discusses potential directions for enhancing ISMS auditing practices by drawing on global experience and offers practical recommendations for improving audit effectiveness and strengthening national cybersecurity frameworks.The findings of this study are of practical relevance to information security professionals, auditors, researchers, and organizations involved in risk management and data protection within the context of ongoing digital transformation.