Smart Cities attract significant attention and investment from government and private entities, leading to their rapid development. With such growth in Smart Cities, data volume and diversity increase due to Internet of Things (IoT) sensors in devices. However, this abundance of data exposes millions of vulnerable devices to cyber threats, risking compromised security and sensitive information. To address these risks, Smart Cities utilize Intelligent Operations Centers (IOCs) equipped with Machine Learning (ML) algorithms. These algorithms continuously monitor and protect against security incidents. Although Quantum Computing (QC) has shown promise in Smart City applications, its usage as a service is still in the early stages. In this paper, we propose to investigate the utilization of Quantum as a Service (QaaS) to develop an architecture for securing a Smart City. Our approach employs Quantum Classifiers, QBoost from D-Wave Leap Quantum Cloud and Variational Quantum Classifier, and PegaSoS Quantum Support Vector Classifier from IBM Quantum Services. These provide real-time data classification and a user-friendly dashboard to display security incidents in the Smart City. Among the three quantum classifiers considered for the proposed architecture, QBoost performed the best both regarding quality and processing time.
Smart Cities attract significant attention and investment from government and private entities, leading to their rapid development. With such growth in Smart Cities, data volume and diversity increase due to Internet of Things (IoT) sensors in devices. However, this abundance of data exposes millions of vulnerable devices to cyber threats, risking compromised security and sensitive information. To address these risks, Smart Cities utilize Intelligent Operations Centers (IOCs) equipped with Machine Learning (ML) algorithms. These algorithms continuously monitor and protect against security incidents. Although Quantum Computing (QC) has shown promise in Smart City applications, its usage as a service is still in the early stages. In this paper, we propose to investigate the utilization of Quantum as a Service (QaaS) to develop an architecture for securing a Smart City. Our approach employs Quantum Classifiers, QBoost from D-Wave Leap Quantum Cloud and Variational Quantum Classifier, and PegaSoS Quantum Support Vector Classifier from IBM Quantum Services. These provide real-time data classification and a user-friendly dashboard to display security incidents in the Smart City. Among the three quantum classifiers considered for the proposed architecture, QBoost performed the best both regarding quality and processing time.
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.
The evolution of smart cities will contribute to an increase in the use of interconnected physical/digital ecosystems, and consequently, an increase in threat surface for the cyber threats against the protected data streams. With the emergence of quantum-based cyberthreats, this has increased the potential range of types of cyber-attacks on protected data and increased the use of quantum-based means of conducting cyber-attacks on protected data. This research proposal presents an Internet of Things (IoT) smart city framework founded on quantum-secure standards that were developed for next-generation IoT smart cities. The IoT Smart City Framework is comprised of four levels of architecture: a Quantum Backbone, a Key Management Level, an Edge Gateway Level, and an IoT Device Layer. Consequently, through the use of this architecture, the IoT Smart City Framework can protect the IoT Smart City Network from future cybercriminal attacks while providing assurance that the IoT Smart City Framework has been 'future-proofed.' By utilizing strategically deployed quantum nodes, the proposed solution performs QKD and frees devices with limited resources from needing to perform highly computationally intensive cryptography in order to secure encrypted communications, instead employing Lightweight Encryption Standards in conjunction with Symmetric Keys established via QKD. Using simulated quantum channels and urban IoT workloads, our experimental results yield secret key rates between 12 and 18 kilobits/sec (kbps) with a low quantum bit error rate (QBER) of 1.8% to 3.2%. Based on these experimental results, we conclude that the proposed hierarchical security architecture provides a resilient security framework for urban essential services, with negligible energy consumption.
Rapid development of smart cities is critically dependent on intelligent systems that can integrate huge networks of sensors, IoT devices, and data-driven services. It is within this context that new paradigms, such as quantum-enhanced deep learning, have the potential to revolutionise security in smart urban environments. This paper reviews the benefits of combining quantum computing with deep learning to enhance the security of smart cities. Quantum-accelerated algorithms can provide enhanced pattern recognition and encryption, thus allowing for quicker detection of anomalies, more effective threat prediction, and better privacy preservation. Finally, this work identifies the current challenges-hardware constraints, model interpretability, and scalability-pointing out future research directions toward the creation of robust quantum-intelligent security architectures for smart city ecosystems.
In increasingly urbanizing surroundings, smart cities (SCs) are essential for improving urban living, addressing resource constraints, optimizing infrastructure, and utilizing technology for efficiency, sustainability, and a higher standard of living. Because quantum cryptography (QC) may offer appropriate performance for security for various applications, it is gaining importance in both the scientific and business areas. SCs produce a huge deal of data because of the extensive use of networks, sensors, and linked devices. Thus, SC security issues include cyber threats, data security, Internet of Smart City Things (IoSCT) vulnerabilities, and urban infrastructure dangers, necessitating strong solutions to protect residents, digital assets, and vital services. Some options include cybersecurity measures, data signatures, threat detection strategies, and standardized security procedures to promote a robust and safe SC ecosystem. Unfortunately, existing systems are still vulnerable to security threats, mainly due to the lack of fast and effective security techniques. In order to combat these security loopholes, this research work proposes a SC security system (PSCSS), grounded in QC, FALCON signatures (FS, QKD) technology, RF and QBoost. Use of QC helps in effective precautions to be maintained for security. In addition, because FS does provide reliable safety to protect the data received through the SC nodes from tampering. In this paradigm, QKD underpins the PSCSS, offering public and private keys to all SC nodes reliably and securely. To sum up, the fusion of RF and QBoost is beneficial when it comes to identifying suspicious data in PSCSS. The viability of the proposed system is illustrated through theoretical performance, security analysis and comparison of the SC system with state-of-theart security algorithms. The theoretical results show that our system achieves a better trade-off between performance and security than existing systems.
The rapid expansion of the Internet of Things (IoT) has transformed the way physical devices communicate, process information, and provide intelligent services.With billions of interconnected devices generating enormous volumes of heterogeneous data, conventional computing architectures increasingly face limitations in computational efficiency, security, optimisation, and scalability.Quantum computing has emerged as a disruptive paradigm capable of solving computationally intensive problems exponentially faster than classical computers for selected problem classes.Integrating quantum computing with IoT offers unprecedented opportunities for intelligent decision-making, real-time optimisation, secure communication, and enhanced machine learning.Quantum-enhanced IoT (QIoT) combines quantum algorithms, quantum machine learning, quantum communication, and post-quantum cryptography to create highly efficient and secure smart systems.Applications span healthcare, industrial automation, smart cities,
ABSTRACT Quantum Computing (QC) has emerged as a promising paradigm capable of solving complex computational problems more efficiently than classical approaches. Quantum‐inspired algorithms provide highly efficient, optimized solutions that can revolutionize various industries by enabling faster data processing, secure communications, and innovative advancements. In cloud security, Machine Learning (ML) techniques play an essential role in detecting and mitigating cyber threats. The integration of QC with ML, known as Quantum Machine Learning (QML), introduces novel security solutions that enhance the resilience of cloud infrastructures against cyberattacks. Moreover, incorporating Generative AI into QML frameworks can further bolster cloud security through advanced anomaly detection, automated response generation, and the synthesis of secure synthetic data for training models. Additionally, in IoT‐enabled sustainable smart cities, information fusion techniques (including multi‐sensor and multi‐process fusion) are essential for ensuring robust cloud security, which is paramount to protecting interconnected systems, data privacy, and critical infrastructure. This review article provides a comprehensive survey of QML‐based approaches, augmented by generative AI and information fusion, for enhancing cloud security in these environments. We review over 150 research articles, examining the key principles of QML, its advantages over classical ML techniques, and its applications in securing cloud environments. Furthermore, the paper discusses the challenges and opportunities in implementing QML, generative AI, and information fusion for cloud security within smart city ecosystems. By offering a detailed analysis, this study aims to contribute to the advancement of quantum‐driven security frameworks for next‐generation smart cities.