Abstract : This article explores the methodologies and applications of synthetic data generation in machine learning (ML). Synthetic data, a pivotal alternative to real-world datasets, addresses challenges such as data biases, privacy concerns, and limited accessibility. The study highlights advanced techniques like generative adversarial networks (GANs), procedural generation, and diffusion models, examining their strengths, weaknesses, and practical applications. A comparative analysis of these methods is presented, along with insights into their integration into ML workflows. The article also discusses the future prospects of synthetic data in emerging fields, including augmented reality, robotics, and digital twins. Ethical considerations, such as data authenticity and potential misuse, are emphasized, advocating for transparent and accountable synthetic data practices. The research underscores synthetic data's transformative potential in enabling robust and scalable machine learning models across industries.

Abstract : Blockchain technology, known for its decentralized nature and cryptographic security, is widely adopted in industries such as finance, supply chain, and healthcare. However, ensuring the reliability of blockchain networks remains a significant challenge as their integration into mission-critical applications grows. This paper analyzes the reliability of blockchain networks by examining key elements such as consensus mechanisms, scalability, and security. Through a comparison of popular blockchain systems such as Bitcoin, Ethereum, and Ripple, the paper investigates their performance and resilience under varying conditions. The study also highlights the potential of layer 2 solutions to enhance scalability and transaction throughput. Overall, this research provides insights into the factors influencing blockchain reliability, offering a foundation for future improvements in blockchain-based applications.

Abstract : This paper explores the application of neural networks (NNs) for predicting information threats in cybersecurity. With the rapid growth of digital technologies and the increasing complexity of cyber threats, traditional security methods, such as rule-based and signature-based systems, are becoming less effective. NNs, with their ability to learn from large datasets and identify intricate patterns, offer a promising approach to detect previously unknown or evolving attack vectors. The study implemented and tested a simple feedforward neural network model to classify network traffic as benign or malicious. The results demonstrated high model accuracy, but also highlighted challenges related to class imbalance in the data. Techniques such as oversampling, regularization, and hyperparameter optimization were proposed to improve the results. This paper emphasizes the importance of neural networks as a tool for building more reliable and effective cybersecurity systems.

Abstract : The article focuses on the study of stream data processing algorithms in the Internet of Things (IoT). It explores modern approaches such as distributed computing and machine learning that ensure high efficiency and accuracy of data processing in real time. Special attention is given to the use of parallel data processing systems, such as Apache Flink and Apache Kafka, as well as classification algorithms for anomaly detection in stream data. The article discusses approaches to adapting algorithms to data changes and scalability methods that enhance system performance. The application of machine learning methods, including random forests and deep learning, has achieved high prediction accuracy and enabled rapid response to changes in IoT systems. The findings indicate that the use of these methods significantly improves the performance of IoT systems in real-time operations.

Abstract : This article explores current methods and technologies applied for optimizing data storage in distributed storage systems. Various approaches are analyzed, including data compression algorithms, methods for data distribution across nodes, and modern algorithms for processing large datasets. Special attention is given to optimization through machine learning and stream processing. The advantages and disadvantages of each method are described, providing insights into their effectiveness and applicability across different fields such as cloud computing, the Internet of Things, and fintech. The focus is on integrating various technologies to improve performance, scalability, and fault tolerance of distributed data storage systems.

Abstract : Big Data analysis has become an integral part of modern business and scientific research. This article explores various Big Data analysis methods that can enhance decision-making accuracy in sectors such as healthcare, finance, and e-commerce. Special attention is given to the use of machine learning, artificial intelligence, and blockchain-based approaches to improve analytics quality and develop more informed strategies. The article also discusses the challenges organizations face when integrating these technologies into existing decision-making frameworks. A comparative analysis of traditional data analysis methods and Big Data techniques is presented, along with real-world examples of their successful applications. This provides a deeper understanding of how data analysis technologies can improve decision accuracy and responsiveness across different industries.

Abstract : This article examines modern clustering methods used for anomaly detection in complex data. The focus is on algorithms such as K-means, DBSCAN, and hierarchical clustering, which are effectively applied to identify anomalous data in various fields, including cybersecurity, financial analytics, and healthcare. Examples of applying these methods to data analysis are provided, along with a description of their key features and differences. Particular attention is given to the K-means algorithm, which clusters data based on similar characteristics, and the DBSCAN algorithm, which detects outliers and anomalies without the need to predefine the number of clusters. Hierarchical clustering is explored as an approach for more in-depth data analysis, especially when uncovering complex relationships between objects. The article also includes Java code examples demonstrating the implementation of various clustering methods. The analysis shows that the choice of method depends on the characteristics of the data and the goals of the study, as well as factors such as data size, density, and the presence of outliers. Recommendations for selecting a clustering method aim to enhance the accuracy and efficiency of anomaly detection in real-world applications.

Abstract : This article examines key methods for ensuring data confidentiality in cloud services, including encryption, tokenization, and multi-factor authentication. Special attention is given to modern cryptographic techniques such as zero-knowledge encryption and homomorphic encryption, as well as threat monitoring systems such as SIEM. The role of regulatory acts, such as GDPR and CCPA, in cloud data protection is also discussed. The article includes examples of using blockchain technologies to ensure data integrity and improve security system reliability in cloud services.
The article emphasizes the importance of a comprehensive approach to data protection, incorporating not only technical solutions but also organizational measures such as regular security audits and compliance with standards. The use of artificial intelligence and machine learning to enhance threat monitoring and response to security incidents is also addressed. The development of technologies and the implementation of automated data protection systems represent an important step toward increasing user and customer trust.
The findings of the research may be useful for organizations utilizing cloud services as well as information security specialists developing and implementing new methods for protecting data in cloud environments.