Securing big data in power plants is an important and fundamental step in the infrastructure of smart cities. In addition, it becomes a barrier if it is not controlled from the beginning. Security must be a combination of fast and robust properties. This research presents a traffic security system (TSS) in a smart city (SC). It is a novel paradigm meant to improve data security and integrity by means of a multilayered method. Advanced fault analysis, dual-stage pseudonymizing, quantum key distribution via the BB84 protocol, and Falcon signatures (FS) are combined in the proposed system. TSS enhances data security by enhancing privacy, supporting resilience against quantum computing threats, and not burdening the network with complex and large keys. The proposed system has been tested against several recently known attacks, such as replay, supply chain, Sybil, blackhole, eavesdropping, advanced persistent threat (APT), tampering, ransomware identity fraud, and desynchronization, and it has been proven to overcome them. In terms of performance evaluation, the average signing time was approximately 0.002 milliseconds. In comparison, the average signature verification time was approximately 0.004 milliseconds, with an average execution time of approximately 0.54 milliseconds and a precision of approximately 93.6 %, a recall of approximately 97.8 %, and an F1_score of approximately 95.6 %, which are considered low compared with those of state-of-the-art research. Thus, the proposed TSS system is highly acceptable for power plant applications. This work lays the groundwork for future developments in safe, privacy-conscious urban systems by presenting a multilayered approach to secure energy data in smart cities.
Securing big data in power plants is an important and fundamental step in the infrastructure of smart cities. In addition, it becomes a barrier if it is not controlled from the beginning. Security must be a combination of fast and robust properties. This research presents a traffic security system (TSS) in a smart city (SC). It is a novel paradigm meant to improve data security and integrity by means of a multilayered method. Advanced fault analysis, dual-stage pseudonymizing, quantum key distribution via the BB84 protocol, and Falcon signatures (FS) are combined in the proposed system. TSS enhances data security by enhancing privacy, supporting resilience against quantum computing threats, and not burdening the network with complex and large keys. The proposed system has been tested against several recently known attacks, such as replay, supply chain, Sybil, blackhole, eavesdropping, advanced persistent threat (APT), tampering, ransomware identity fraud, and desynchronization, and it has been proven to overcome them. In terms of performance evaluation, the average signing time was approximately 0.002 milliseconds. In comparison, the average signature verification time was approximately 0.004 milliseconds, with an average execution time of approximately 0.54 milliseconds and a precision of approximately 93.6 %, a recall of approximately 97.8 %, and an F1_score of approximately 95.6 %, which are considered low compared with those of state-of-the-art research. Thus, the proposed TSS system is highly acceptable for power plant applications. This work lays the groundwork for future developments in safe, privacy-conscious urban systems by presenting a multilayered approach to secure energy data in smart cities.
This chapter attempts to present a Post-Quantum Secure Smart City Electricity Data (PQSED) system, which generates a quantum key via the BB84 protocol and digitally signs the data using FALCON, and uses pseudonym technology to add more security and protection to the data and digital signature, and also uses Walrus Optimization Algorithm (WOA) technology to the data to detect any abnormal behavior. The proposed system has been tested against several recently known attacks, such as replay attacks, Sybil attacks, eavesdropping, advanced ransomware attacks, social engineering attacks, and critical infrastructure attacks, and it has been proven to overcome them. In terms of performance evaluation, we obtained an average signature time of about 0.004 MS, an average signature verification time of about 0.006 MS, an average execution time of 0.66 MS, a precision of 96%, a recall of 95%, and an F1-score of 96%, which are good compared to the state-of-the-art research. Thus, our proposed system is highly acceptable for power plant applications.
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 progress of quantum computing poses significant challenges to traditional cryptographic mechanisms, necessitating the adoption of post-quantum cryptography (PQC) solutions. This paper proposes a Quantum-Enhanced Security for Smart Meters (QESM) system to protect power plant data in smart cities, integrating Kyber for secure key exchange, FALCON (Fast-Fourier Transform over Lattice-based Cryptography) for quantum-resistant digital signatures, and ZKP (Zero-Knowledge Proof) for effective verification without revealing sensitive data to secure power plant data against quantum attacks. To evaluate the security of the proposed system, we analyze its resistance to various quantum threats, including Shor’s algorithm, Grover’s algorithm, quantum key analysis, quantum reversal encryption, quantum amplification, quantum switching, and quantum collision attacks. In the current study, accurate measures were used and the average was approximately 7.065 (bits/byte) for randomness, the average execution time was 6.202 milliseconds, the average memory consumption was approximately 4.343 KB, 6.4 Completeness was equal to 1 and unforgeability was 100%. As for the average throughput, it was approximately 485,605 operations per second. That shows the QESM system provides strong security and efficiency, making it a viable solution for protecting the electricity infrastructure in smart cities in the quantum era.
The rapid advancement of quantum computing poses significant challenges to conventional cryptography, necessitating the adoption of post-quantum cryptography (PQC) solutions. This chapter proposes a Post-Quantum Lattice Security (PQLS) system for protecting power plant data in smart cities. It integrates Kyber for secure key exchange, Falcon for quantum-resistant digital signatures, ZKP for efficient authentication without revealing sensitive data, and JSON-LD for standardizing the format of data received from different smart meters. To evaluate the security of the proposed framework, we analyze its resistance to various threats, such as side-channel and message recovery attacks. We measured key performance indicators. The results showed an average CPU utilization of 2.4592 MS, memory consumption averaging 1843.899 KB, an execution time of 2.45 MS, and a level averaging 66.27677. This demonstrates that our proposed system offers high security and efficiency, making it a practical solution for protecting electrical infrastructure in smart cities in the quantum era.
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.
Smart cities are based on the common digital infrastructures to make cities more efficient, sustainable, and quality of life, yet the data-driven nature of such integration exposes an enormous threat to security and privacy. Other traditional methods of cybersecurity and deep learning have weaknesses of scalability, strength, and resistance to advanced and future threats. In this chapter, the authors examine the concept of Quantum-Enhanced Deep Learning as a prospective security paradigm of smart city ecosystems. The chapter proves the accuracy of improvement of intrusion detection and communication efficiency by referring to recent case studies of intelligent transportation systems and smart grids when applying hybrid classical-quantum models. The results include the potential of quantum-resilient intelligence to enhance anomaly detection, distributed security operations, and future-ready urban cybersecurity architectures and overcome the challenge of operational and governance.