Introduction. The paper is devoted to the digitalization of the industry and the safety of construction. The urban economy includes systems, for example, water supply and sanitation, HVAC and others, the reliability and stability of which are closely related to the level of cybersecurity. In the context of digitalization and the widespread adoption of BIM and automated process control systems, the level of their exposure to cyber threats is significantly increasing. Traditional approaches to security are not effective enough. The practical application of quantum communications and artificial intelligence methods in the field of cybersecurity remains poorly understood. The purpose of this study is to analyze the potential of using quantum technologies and artificial intelligence methods to improve the cybersecurity of BIM and automated process control systems, as well as to assess the prospects for implementing solutions to protect critical infrastructure. Materials and methods. The work uses methods of system analysis, modelling of cybersecurity architectures, as well as machine learning methods to identify anomalies in network traffic and telemetry of BIM and automated process control systems. The technology of quantum key distribution, which provides physically guaranteed confidentiality of communication channels, is considered as a cryptographic basis. Results. A hybrid architecture of BIM and automated process control systems cyber defense technologies is proposed, combining quantum technologies to protect communications and intelligent monitoring based on machine learning to detect abnormal behavior. It is shown that the integration of quantum networks with AI can significantly increase the stability of the system, as well as reduce the time for incident detection. Conclusions. The results of the study confirm the prospects for the joint use of quantum communications and artificial intelligence to protect BIM, automated process control systems. The proposed approach can be used in the design of cybersecurity systems and serves as the basis for further research in this area.
Introduction. The paper is devoted to the digitalization of the industry and the safety of construction. The urban economy includes systems, for example, water supply and sanitation, HVAC and others, the reliability and stability of which are closely related to the level of cybersecurity. In the context of digitalization and the widespread adoption of BIM and automated process control systems, the level of their exposure to cyber threats is significantly increasing. Traditional approaches to security are not effective enough. The practical application of quantum communications and artificial intelligence methods in the field of cybersecurity remains poorly understood. The purpose of this study is to analyze the potential of using quantum technologies and artificial intelligence methods to improve the cybersecurity of BIM and automated process control systems, as well as to assess the prospects for implementing solutions to protect critical infrastructure. Materials and methods. The work uses methods of system analysis, modelling of cybersecurity architectures, as well as machine learning methods to identify anomalies in network traffic and telemetry of BIM and automated process control systems. The technology of quantum key distribution, which provides physically guaranteed confidentiality of communication channels, is considered as a cryptographic basis. Results. A hybrid architecture of BIM and automated process control systems cyber defense technologies is proposed, combining quantum technologies to protect communications and intelligent monitoring based on machine learning to detect abnormal behavior. It is shown that the integration of quantum networks with AI can significantly increase the stability of the system, as well as reduce the time for incident detection. Conclusions. The results of the study confirm the prospects for the joint use of quantum communications and artificial intelligence to protect BIM, automated process control systems. The proposed approach can be used in the design of cybersecurity systems and serves as the basis for further research in this area.
Quantum AI is emerging as a groundbreaking solution in the field of cybersecurity, particularly for industrial automation. With the increasing sophistication of cyber threats, traditional security measures are becoming inadequate to protect critical infrastructure. This chapter explores the integration of quantum computing and artificial intelligence (AI) to fortify industrial security. It discusses key concepts such as quantum cryptography, AI-driven threat detection, and real-time anomaly monitoring. The chapter also highlights the challenges associated with implementing quantum AI in industrial settings and outlines future research directions. By utilizing quantum AI, industries can achieve unprecedented levels of security, ensuring robust protection against evolving cyber threats.
Rapid advances in artificial intelligence (AI) and quantum computing can potentially transform cybersecurity. It explores the synergistic integration to improve cyber defenses. The ability of AI to analyze data, detect anomalies, and perform predictive analytics, combined with the potential of quantum computing to solve complex cryptographic problems, provides a strong framework for today's cybersecurity challenges. Using quantum algorithms and machine learning techniques, it aims to strengthen cryptographic methods, optimize threat detection systems, and develop flexible defense protocols against sophisticated cyber attacks. The study also examines the implications of quantum cryptography and the role of AI in managing quantum-generated data It discusses the challenges and ethical considerations associated with applying these advanced technologies in cybersecurity infrastructures. The findings suggest that the convergence of AI and quantum computing can significantly improve the effectiveness and adaptability of cybersecurity measures, paving the way for a secure digital future.
Cybersecurity is crucial and has a worldwide significant impact in several industries that must be addressed to preserve, protect, and uphold the integrity of any system. Quantum artificial intelligence is a combination of quantum computing and machine learning techniques. It can be used for various cybersecurity applications that require advanced methodologies for cyberattack detection and prevention. Nowadays, traditional computers are reaching their limits and are becoming less effective against advanced attacks, which enables further requirements of quantum-based algorithms. The computational power of quantum computing can greatly reduce cybersecurity threats by facilitating advanced threat detection, rapid information processing, and automation of safety protocols that are extremely difficult to do with classical computing techniques. This chapter outlines the approach, future scope, hurdles, and areas of implementing quantum advancements to make our cyber defense system more sophisticated against cyber threats powered by quantum computers.
Currently and in the near future, Smart Cities are vital to enhance urban living, address resource challenges, optimize infrastructure, and harness technology for sustainability, efficiency, and improved quality of life in rapidly urbanizing environments. Owing to the high usage of networks, sensors, and connected devices, Smart Cities generate a massive amount of data. Therefore, Smart City security concerns encompass data privacy, IoT vulnerabilities, cyber threats, and urban infrastructure risks, requiring robust solutions to safeguard digital assets, citizens, and critical services. Some solutions include robust cybersecurity measures, data encryption, AI-driven threat detection, public-private partnerships, standardized security protocols, and community engagement to foster a resilient and secure smart city ecosystem. For example, Security Information and Event Management helps in real-time monitoring, threat detection, and incident response by aggregating and analyzing security data. In this paper, we propose a Hybrid Quantum Architecture for bolstering Smart City security that exploits Quantum Machine Learning and SIEM to provide security based on Quantum Artificial Intelligence and patterns/rules. The validity of the architecture was proven by conducting experiments and a comparison of the QML algorithms with a state-of-the-art AI algorithms. We also provide a proof of concept dashboard for the proposed architecture.
Quantum computing and artificial intelligence (AI) converge as a phenomenon that is changing cybersecurity defense mechanisms, providing both unprecedented opportunities and emergent challenges. With classical encryption becoming more susceptible to quantum-driven attacks, resilient quantum-safe protocols and AI-driven threat detection systems have become highly demanded. Recent studies point to the disruptive nature of quantum technologies in compromising traditional cryptography systems and, at the same time, provide novel countermeasures to disruptive technologies in the form of quantum cryptography, blockchain, and adaptive machine learning systems. The rising AI improves the active threat-hunting, zero-trust, and predictive analytics to detect advanced persistent threats in real-time across cloud, hybrid, and cyber-physical ecosystems. In the meantime, moral issues, institutional preparedness, and technological asymmetry pose obstacles to extensive adoption. The potential for a future-proof digital infrastructure with strategic integration of AI and quantum computing, advancing national security, and collaborating with partners around the world across the rapidly changing cyber threat could be a path forward. This article aims to critically assess the state of cybersecurity in the future by reviewing different academic and professional perceptions and experiences.
The article examines key issues related to ensuring the security of artificial intelligence systems in the context of digital economic transformation and the development of quantum computing technologies. It is shown that the largescale integration of AI into economically significant processes increases cryptographic, informational, and institutional risks associated with the potential vulnerability of classical security mechanisms to quantum attacks. The study analyzes major threats to AI systems, including cryptographic weaknesses, risks of data and machine learning model compromise, and the longterm «store now, decrypt later» threat scenario. Quantumresistant approaches to protecting artificial intelligence systems are systematized, covering postquantum cryptography, hybrid security schemes, and architectural solutions for AI platforms. Special attention is paid to the economic and institutional aspects of AI security, emphasizing the need for proactive adaptation of cybersecurity mechanisms and the development of regulatory frameworks that account for emerging quantum threats. Based on the analysis, authorial recommendations are proposed to enhance the resilience of artificial intelligence systems and mitigate longterm risks to the digital economy.