It has been recognized that Blockchain technology contributes to environmentally sustainable development goals (SDGs). It has emerged as a disruptive innovation capable of transforming various economic and social sectors significantly. This conceptual paper is driven by the need to explore how blockchain, specifically a consortium-based Ethereum architecture, can be integrated into higher education institutions to ensure data sovereignty, integrity, and verifiability while adhering to legal and ethical standards such as GDPR. We propose a multi-layered blockchain-based model for Kazakhstan’s Unified Platform of Higher Education (UPHE). This model employs hybrid on-chain/off-chain data storage, smart contract automation, and a Proof-of-Authority consensus mechanism to address system limitations, including data centralization and inadequate verification of academic credentials. Empirical simulations using Blockscout and Ethereum-compatible tools demonstrate the model’s feasibility and performance. This paper contributes to the growing discussion on educational blockchain applications by presenting a scalable, secure, and transparent architecture that aligns with institutional governance and Environmental, Social, and Governance (ESG) principles. It also supports the objectives of UN SDG 4 (i.e., Quality education) by fostering trust, transparency, and equitable access to verifiable educational credentials.

This study examines the development of a model for the threat detection system with the use of machine learning and neural network methods. The fast development of Internet technologies has led to the appearance of many digital systems and platforms. However, despite the impressive technological progress, another side also emerged in the spread of a massive number of different cyber threats. Although various ways have been created to detect and prevent them, the threats are also developing and becoming more complex each year. Therefore, new system defense and data protection methods using machine and deep learning approaches have been proposed recently. The methods based on these approaches have proved to be especially effective in the wave of new Artificial Intelligence applications. In this paper, a threat detection system has been designed to disclose different kinds of threats while maintaining the security, confidentiality, and availability of the computer system. The development of machine learning models for detecting DDoS and man-in-the-middle attacks, Structured Query Language (SQL) injections, phishing, and malware was examined. The data scaling, feature selection, feature extraction, and classification steps were also thoroughly described. Naïve Bayes, Logistic Regression, Decision Tree, Random Forest, XGBoost, CatBoost, and Deep Neural Network algorithms were utilized for training the cyber threat detection models. The experimental results evaluated all the models using accuracy, precision, recall, and F1-score metrics. The best models achieved scores in the range of 0.90 to 1.00.

Information technology is a significant factor in the modern world. This area is developing quickly, becoming essential as new, more complex protection methods appear. Threat detection instruments have also been actively developing for recent decades. The available scanning systems allow to disclose many threats. Moreover, machine and deep learning Artificial intelligence methods have been developing significantly. With so many penetration testing scanners available, choosing the most effective scanner can be daunting as it depends on the specific testing environment. This paper focuses on detecting SQL injection threats with the use of such machine learning algorithms as Naïve Bayes, Support Vector Machine, Decision Tree, Random Forest, XGBoost, and CatBoost. The analyzed dataset contains safe and dangerous SQL commands that various websites have been exposed to. The classification model results showed that most applied algorithms demonstrated perfect accuracy, precision, recall, and F1-score values above 0.95. These high scores proved to be efficient in the SQL injection classification tasks.

A model of an automated system for secure cross-border information exchange is considered. This automated system presents as a complex of the following modules which ensure the information security: data encryption, an electronic digital signature (EDS), access control to the stored information based on two-factor authentication, resolution of possible conflict situations. In this paper, models of two modules for an automated system are described: the electronic digital signature and access control to information based on two-factor authentication. The mathematical model of the formation and verification of the digital signature is described in detail. The phased software implementation of this model with the analysis of the results is described. The two-factor authentication algorithm based on the authenticator program and a mobile phone is considered. A secret string generator based on the method of exhaustive search was carried out. A generator of trigonometric functions is described, which is used to calculate a one-time password. The phased software implementation of this model is given. The analyzed results of the algorithm are presented.

In the modern world, medical data leakage has many external and internal threats. Information systems of medical organizations are constantly subject to various types of cyber-attacks and unauthorized penetration attempts, which leads to the publication of patient medical data online. Existing authentication schemes using blockchain technologies in medical organization systems ensure the integrity of medical data and secure access to patient data. However, one of the serious reasons for unauthorized access to the healthcare system is the human factor, which manifests itself in a negligent attitude towards account security, non-compliance with the rules and policies of information security, and transferring to third parties personal login details to the information system of a medical organization. This paper proposes a solution to this problem through an improved two-step authentication scheme using cloud technology and blockchain. The combined use of cloud technologies and blockchain is a distinctive feature of the proposed authentication scheme since it provides two levels of protection: 1) two-step authentication, the second stage of which includes biometrics through a mobile application. It prevents unauthorized access to the system by third parties; 2) cloud encryption keys for decrypting medical data, which are also accessed through the user's biometrics. The practical part of the paper includes the implementation of biometric login in Python using the OpenCV library. As a result of the practical part, unique fingerprint samples were obtained. The biometric user verification algorithm is designed for a mobile application, which we plan to implement in the future

The object of this research is the security of communication networks, particularly in decentralized, multi-user environments where robust data protection and integrity are critical. The issue under discussion is the rising vulnerability of conventional cryptography systems resulting from ever complex cyberattacks and the expected risks presented by quantum computing possibilities. The development of a QKD protocol employing quantum superposition to improve data security and resilience against both present and future quantum-based cyber-attacks is demonstrated by the achieved results of this work. Achieving scalability and autonomous eavesdropping detection, this protocol lets several communication nodes securely exchange randomly produced keys without centralized management. A quick analysis of the results reveals that main elements influencing the great durability, security, and adaptability of the protocol are quantum superposition and its distributed character. Without centralized authority, the characteristics of the obtained results – especially the use of optical components, detectors, and quantum sources in conjunction with classical communication channels – solve the problem of ensuring data confidentiality and integrity in a multi-user environment. This protocol's practical reach covers safe communication applications in both public and private sectors, therefore addressing situations calling for strong data protection against modern cyberattacks. Conditions for practical application include settings like government, financial, or health-related interactions when safe information flow is crucial. This QKD system offers a future-ready security solution for high-stakes environments and represents notable advancement toward protecting data from quantum and conventional attacks