Machine Learning-Enhanced Advancements in Quantum Cryptography: A Comprehensive Review and Future Prospects

Quantum cryptography has emerged as a promising paradigm for secure communication, leveraging the fundamental principles of quantum mechanics to guarantee information confidentiality and integrity. In recent years, the field of quantum cryptography has witnessed remarkable advancements, and the integration of machine learning techniques has further accelerated its progress. This research paper presents a comprehensive review of the latest developments in quantum cryptography, with a specific focus on the utilization of machine learning algorithms to enhance its capabilities. The paper begins by providing an overview of the principles underlying quantum cryptography, such as quantum key distribution (QKD) and quantum secure direct communication (QSDC). Subsequently, it highlights the limitations of traditional quantum cryptographic schemes and introduces how machine learning approaches address these challenges, leading to improved performance and security. To illustrate the synergy between quantum cryptography and machine learning, several case studies are presented, showcasing successful applications of machine learning in optimizing key aspects of quantum cryptographic protocols. These applicatiocns encompass various tasks, including error correction, key rate optimization, protocol efficiency enhancement, and adaptive protocol selection. Furthermore, the paper delves into the potential risks and vulnerabilities introduced by integrating machine learning with quantum cryptography. The discussion revolves around adversarial attacks, model vulnerabilities, and potential countermeasures to bolster the robustness of machine learning-based quantum cryptographic systems. The future prospects of this combined field are also examined, highlighting potential avenues for further research and development. These include exploring novel machine learning architectures tailored for quantum cryptographic applications, investigating the interplay between quantum computing and machine learning in cryptographic protocols, and devising hybrid approaches that synergistically harness the strengths of both fields. In conclusion, this research paper emphasizes the significance of machine learning-enhanced advancements in quantum cryptography as a transformative force in securing future communication systems. The paper serves as a valuable resource for researchers, practitioners, and policymakers interested in understanding the state-of-the-art in this multidisciplinary domain and charting the course for its future advancements

АНАЛІЗ ЗАСТОСУВАННЯ ГІБРИДНИХ КРИПТО-КОДОВИХ КОНСТРУКЦІЙ ДЛЯ ПІДВИЩЕННЯ РІВНЯ СТІЙКОСТІ ГЕШ-КОДІВ ДО ЗЛОМУ

The article presents a new way to increase the cryptographic strength of MAC codes for messages transmitted over the Internet. Today, this should make it possible to resist both the consequences of cyber threat aggregation and increase the speed of unauthorized access to data through the creation of such new hardware capabilities as quantum computer technology. The paper proposes to consider the application of the modified UMAC algorithm on modified McEliece elliptic curves using crypto-code structures with hybridity features. The proposed structures were tested for collision properties. For this, a software application was developed in the environment of the object-oriented programming language C#. To determine the capabilities of the studied hash-codes, a complex indicator of the effectiveness of the modified UMAC algorithm was developed. This made it possible to test the proposed designs for resistance to hacking, consider the value of the data that is being protected, and the safe time for a possible hack. As a way to assess this indicator, it was proposed to use the method of multivariate complex analysis. For this, scales for measuring and interpreting each indicator were developed. It has been proven that this method of evaluation allows one to obtain adequate results and combine them with the results of accurate calculations for individual parameters. The issue of reducing energy costs for the formation of crypto-code structures was also investigated. The results showed that the creation of hybrid crypto-code structures leads to the lowest energy costs.У статті представлено новий спосіб підвищення крипостійкості МAC-кодів повідомлень, що передаються через Інтерне-мережі. На сьогодні це має дозволити протистояти не тільки наслідкам комплексування кіберзагроз а й підвищенню швидкості несанкціонованого доступу до даних через створення таких нових апаратних можливостей, як квантова комп'ютерна технік. В роботі пропонується розгляд застосування модифікованого алгоритму UMAC на модифікованих еліптичних кривих Мак-Еліса із використанням крипто-кодових конструкцій з ознаками гібридності. Проведено перевірку запропонованих конструкцій на колізійні властивості. Для цього було розроблено програмний додаток в середовищі об'єктно-орієнтованої мови програмування C#. Для виявлення можливостей геш-кодів, що досліджувалися, з точки зору їх стійкості до злому, цінності даних, у захисті яких вони повинні застосовуватися та з врахуванням безпечного часу до можливого злому, було розроблено комплексний показник ефективності модифікованого алгоритму UMAC. У якості метода оцінки даного показника було запропоновано використовувати метод багатофакторного комплексного аналізу. Для цього були розроблені шкали вимірювання та інтерпретації кожного показника. Доведено, що даний метод оцінки дозволяє отримати достатньо адекватні результати та поєднати їх з результатами точних розрахунків за окремими параметрами. Також було досліджено питання зниження енергетичних витрат для формування крипто-кодових конструкцій, за результатами якого було доведено, що створення саме гібридних крипто-кодових конструкцій призводить до найменших енергетичних витрат

The Potential Benefits and Challenges of a BRICS+ Agency for Cybersecurity Intelligence Exchange

The Brazil, Russia, India, China, South Africa (BRICS) nations lack a cohesive cybersecurity framework for intelligence exchange. The proposed expansion of the BRICS bloc calls for a BRICS+ agency dedicated to cybersecurity information sharing and analysis. Information Sharing and Analysis Centres (ISACs) are successful not-for-profit entities that centralise resources for gathering, analysing, and disseminating cybersecurity intelligence. However, founding a BRICS+ ISAC confronts challenges such as coordination complexity, financial constraints, trust deficits, linguistic diversity, and disparate legislative landscapes. This paper proposes a novel hybrid ISAC architectural model that amalgamates centralised and decentralised elements, presenting a tailored solution for the multifaceted needs of the expanding BRICS+ entity. The innovation of this model lies in its capacity to enhance cybersecurity resilience, promote efficient intelligence exchange, elevate the BRICS+ international standing, and solidify inter-nation collaboration, while being flexible enough to cater to the specific legal, cultural, and technological variances across member countries. The proposed model's uniqueness and adaptability position it as the premier choice for actualising the BRICS+ vision for a unified cyber front