Smart City Security Research Articles

Biometrics 2.0 for the Security of Smart Cities

Biometrics 2.0 for the Security of Smart Cities

Security Protection Systems and Technical Challenges in Smart City Construction

With the rapid development of information technology and the internet, smart cities have become a new trend in urban development globally. However, the security challenges faced by smart cities are becoming increasingly severe. The security protection of smart cities involves multiple issues such as cybersecurity, data security, and physical security. It must also balance efficiency and sustainability while safeguarding urban infrastructure and citizens' privacy. This paper analyzes the security protection needs and system construction of smart cities against the backdrop of their development, explores the technical challenges they face, and proposes corresponding strategies. Finally, the paper summarizes the future development direction of smart city security protection, emphasizing the importance of cross-departmental collaboration and technological innovation, offering valuable references for the future security management of smart cities.

A Shark Inspired Ensemble Deep Learning Stacks for Ensuring the Security in Internet of Things (IoT)-Based Smart City Infrastructure

Recent advancements in the Internet of Things (IoT) have paved the way for intelligent and sustainable solutions in smart city environments. However, despite these advantages, IoT-connected devices present significant privacy and security risks, as network attacks increasingly exploit user-centric information. To protect the network against the rapidly growing number of cyber-attacks, it is essential to employ a cognitive intrusion detection system (CIDS) capable of handling complex and voluminous network data. This research presents a novel ensemble deep learning framework designed to enhance cybersecurity in IoT-based smart city ecosystems. The proposed architecture integrates Self-Attention Convolutional Neural Networks, Bidirectional Gated Recurrent Units, and Shark Smell Optimized Feed Forward Networks to create a robust, adaptive system for detecting and mitigating cyber threats. By leveraging fog computing, the model significantly reduces latency and computational overhead, making it highly suitable for large-scale IoT deployments. Extensive experimentation using the ToN-IoT dataset demonstrates the framework's exceptional performance, achieving a 99.78% detection rate across various attack types and an AUC of 0.989. The proposed model outperforms existing state-of-the-art approaches, achieving a mean fitness function value of 0.85640 and a standard deviation of 0.037630 in binary classification outcomes. In multi-class classification, the model maintains a mean fitness function value of 0.8230 and a variance of 2.28930 × 104, significantly outperforming other meta-heuristic algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The model exhibits superior accuracy and efficiency compared to existing state-of-the-art approaches, particularly in identifying complex and emerging threats. This research makes significant contributions by introducing innovative feature extraction techniques, optimizing model performance for resource-constrained environments, and providing a scalable solution for securing smart city infrastructure. The findings highlight the potential of ensemble deep learning approaches to fortify IoT networks against cyberattacks, paving the way for more resilient and secure smart cities.

Distributed and Collaborative Lightweight Edge Federated Learning for IoT Zombie Devices Detection

The fast development of artificial intelligence and Internet of Things (IoT) technologies has enabled various applications of smart cities, e.g., smart monitoring and surveillance. However, vulnerabilities of IoT devices bring new threats to the security of smart cities. To identify ubiquitous IoT botnet attacks, a distributed and collaborative lightweight edge federated learning model for IoT zombie devices detection is proposed, named FIOT. To reduce computational complexity and enhance the adaptability to new attack environment at the network edge, FIOT is designed in a lightweight manner based on feature dimensionality reduction and transfer learning. Three IoT botnet datasets are used to validate the effectiveness of the proposed FIOT. Experimental results show that FIOT has an accuracy loss of less than 3% in terms of F1 value compared to the centralized learning, but the training time of FIOT is only 14.3% of that of centralized learning. While ensuring high detection accuracy, the number of parameters of FIOT is compressed to 37.58% of the comparison method.

The applications of the internet of things in smart cities governance: a bibliometric study

Integrating the Internet of Things (IoT) in smart cities can solve rapid urbanization. It will provide better urban services, addressing urban problems that develop because of enhancing urbanization without establishing policies focusing on well-being. The present investigation explores the growing trend of the dynamic publications associated with the advanced technologies integrated into smart city publication productivity using bibliometric analysis. This approach enables the detection of the usage history, the usage scheme, and the research landscape topic in scientific papers on IoT in smart cities. This study included 976 articles from 2013 to 2023. The area of research in smart cities is categorized into six important clusters: (1) IoT application areas in smart cities, (2) IoT infrastructure for smart cities, (3) IoT security and privacy for smart cities, (4) IoT architecture for smart cities, (5) IoT technologies and communication protocol for smart cities, (6) IoT data analysis for smart cities. Moreover, this study reveals that the issues related to new technologies, security, and privacy of IoT to integrate into smart cities would be an interesting topic for future research.

GRDATFusion: A gradient residual dense and attention transformer infrared and visible image fusion network for smart city security systems in cloud and fog computing

AbstractThe infrared and visible fusion technology holds a pivotal position in smart city for cloud and fog computing, particularly in security system. By fusing infrared and visible image information, this technology enhances target identification, tracking and monitoring precision, bolstering overall system security. However, existing deep learning‐based methods rely heavily on convolutional operations, which excel at extracting local features but have limited receptive fields, hampering global information capture. To overcome this difficulty, we introduce GRDATFusion, a novel end‐to‐end network comprising three key modules: transformer, gradient residual dense and attention residual. The gradient residual dense module extracts local complementary features, leveraging a dense‐shaped network to retain potentially lost information. The attention residual module focuses on crucial input image details, while the transformer module captures global information and models long‐range dependencies. Experiments on public datasets show that GRDATFusion outperforms state‐of‐the‐art algorithms in qualitative and quantitative assessments. Ablation studies validate our approach's advantages, and efficiency comparisons demonstrate its computational efficiency. Therefore, our method makes the security systems in smart city with shorter delay and satisfies the real‐time requirement.

Особенности оценки вредоносной активности в инфраструктуре Умного города на основе гранулирования информации и гранулярных моделей вычислений

The object of the research is a new methodological approach to information granulation and fuzzy granular calculations, as a mathematical and methodological tool for improving the reliability of assessing the level of information security of the Smart City infrastructure. The proposed approach is one of the options for the practical application of elements of the theory of fuzzy sets in the tasks of search, identification and current assessment of signs of time-bearing activity. A detailed analysis of the features of this approach has been carried out, determining the expediency and conditions of its application for assessing malicious activity in the infrastructure of a Smart City. The theoretical aspects of the application of information granulation and fuzzy granular computing to the assessment of malicious activity combining various signs for various categories of potential threats to the infrastructure and subjects of a Smart City - the categories “cyberattack”, “malicious virus threat” or “data leakage (loss)” are studied and described. The analysis of the features of the proposed approach is carried out, which allows taking into account the opinions of experts and eliminating the vagueness associated with noise, disorder and lack of formalization of surveillance data collected and pre-processed in the interests of assessing threats and consequences of negative manifestations of malicious activity. A sequence of calculations and analytical expressions for calculating the estimated values of signs of harmful activity for various categories of potential threats to the infrastructure and subjects of a Smart City has been developed and described in detail. The approach assumes the practical possibility of evaluating signs of malicious activity using information granules formed on the basis of a minimum numerical distance between the values of membership functions characterizing vaguely specified data on the presence or absence of observed signs (attributes) of malicious activity, as well as granular summation and determination of the trace function of the granular sum. At the same time, the proposed approach makes it possible to obtain estimates of signs of malicious activity that are adequate to the tasks of monitoring the Smart City security policy and, ultimately, provides increased reliability of proactive threat control and analysis of the possible consequences of a negative manifestation of suspicious activity.

Hybrid quantum architecture for smart city security

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, Internet-of-Things (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, Artificial Intelligence (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 (SIEM) helps in real-time monitoring, threat detection, and incident response by aggregating and analyzing security data. To this end, no integrated systems are operating in this context. In this paper, we propose a Hybrid Quantum-Classical Architecture for bolstering Smart City security that exploits Quantum Machine Learning (QML) and SIEM to provide security based on Quantum Artificial Intelligence and patterns/rules. The validity of the hybrid quantum-classical architecture was proven by conducting experiments and a comparison of the QML algorithms with state-of-the-art AI algorithms. We also provide a proof of concept dashboard for the proposed architecture.

Prospects of Cyber Security in Smart Cities

Prospects of Cyber Security in Smart Cities

Image-based malware analysis for enhanced IoT security in smart cities

To develop safer smart city solutions, it is crucial to investigate new technologies for malware analysis and detection to enhance existing malware prevention systems. Deep learning (DL) techniques have surpassed conventional machine learning as the dominant method for network security; therefore, it is crucial for researchers to utilise DL techniques to address the velocity, volume, and complexity of modern malware, as these approaches can effectively handle large amounts of data and extract representative information robustly. This study investigates the application of deep learning techniques, specifically different autoencoders (AEs), to improve malware analysis and prevention in Internet of Things (IoT)-based smart cities. This research evaluates different convolutional neural network based AE structures and aims to establish a robust malware analysis method by focusing on image classification. By experimenting on both greyscale and RGB malware imagery datasets, it has been demonstrated that variational AE can not only detect nearly all malware but also illustrate the generalisability and efficacy of AE in addressing malware threats. The study systematically evaluates different AE configurations, and this comparative analysis can inspire further research into deep learning techniques for IoT security measures.

Deep Learning-Enhanced anomaly detection for IoT security in smart cities

The swift expansion of Internet of Things (IoT) devices within smart cities necessitates robust security measures to safeguard critical infrastructure and ensure citizen safety. In response, this research presents an advanced deep learning- based anomaly detection system designed to bolster IoT security within the context of smart cities. Leveraging the IoT-23 dataset, our system demonstrates impressive results. One of the system's notable strengths is its adaptability; it generalizes well to diverse datasets and maintains its efficacy in the presence of adversarial attacks. An intuitive user interface facilitates system management and response to detected anomalies, providing a holistic approach to IoT security in smart cities. Positive user feedback affirms the system's usability and satisfaction, emphasizing its practical utility. This research contributes to the broader field of IoT security. It furnishes well-documented code and resources, laying the groundwork for further advancements in this critical domain. As smart cities continue to evolve, the findings and innovations presented in this research serve as a vital step toward ensuring the integrity, privacy, and reliability of IoT networks within urban environments. Lastly, the findings of the experiments show that this technique has an excellent detection performance, with an accuracy rate which is more than 98.7%.

Enhancing Internet of Smart City Security: Utilizing Logistic Boosted Algorithms for Anomaly Detection and Cyberattack Prevention

Enhancing Internet of Smart City Security: Utilizing Logistic Boosted Algorithms for Anomaly Detection and Cyberattack Prevention

Minimizing Overhead through Blockchain for Establishing a Secure Smart City with IoT Model

Conventional safety measures are inconsistent with inexpensive technologies like the Internet of Things (IoT) due to their significant storage traces, which are prohibitive to their utilization. The blockchain (BC) framework maintains the five essential security primitives: genuineness, credibility, secrecy, accessibility, and non-renunciation. Most IoT gadgets have limited resources, so a traditional blockchain deployment is inappropriate. Traditional deployment of blockchain computing in the Internet of Things leads to significant power consumption, delay, and computational inefficiency. The proposed solution improves the blockchain's conception to serve IoT technologies better. This article proposes a blockchain-based intelligent city design for the IoT that keeps all encryption safety precautions in place. Adding blockchain to an IoT platform does not add much extra labour. After comparing all safety requirements to existing literature, it is clear that the proposed method achieves satisfactory safety effectiveness.

Adaptive Multi-Layer Security Framework (AMLSF) for real-time applications in smart city networks

<p>This study introduces the Adaptive Multi-Layer Security Framework (AMLSF), a novel approach designed for real-time applications in smart city networks, addressing the current challenges in security systems. AMLSF innovatively incorporates machine learning algorithms for dynamic adjustment of security protocols based on real-time threat analysis and device behavior patterns. This approach marks a significant shift from static security measures, offering an adaptive encryption mechanism that scales according to application criticality and device mobility. Our methodology integrates hierarchical key management with real-time adaptability, further enhanced by an advanced rekeying strategy sensitive to device mobility and communication overhead. The paper’s findings reveal a substantial improvement in security efficiency. AMLSF outperforms existing models in encryption strength, rekeying time, communication overhead, and computational time by significant margins. Notably, AMLSF demonstrates an adaptability increase of over 30% compared to traditional models, with encryption strength and computational time efficiency improving by approximately 25%. These results underscore AMLSF’s capability in delivering robust, dynamic security without sacrificing performance. The achievements of AMLSF are significant, indicating a promising direction for smart city security frameworks. Its ability to adapt in real-time to various security needs, coupled with its performance efficiency, positions AMLSF as a superior choice for smart city networks facing diverse and evolving security threats. This framework sets a new benchmark in smart city security, paving the way for future developments in this rapidly advancing field.</p>

Enhanced Dwarf Mongoose optimization algorithm with deep learning-based attack detection for drones

Security in smart cities is a challenging issue in urban environments as they depend upon interconnected technologies and data for effective services. To address security challenges, smart cities implement robust cybersecurity measures, including network monitoring, encryption, and intrusion detection systems. Detecting and mitigating possible security risks in drone network B5G is a crucial aspect of ensuring reliable and safe drone operation. It is necessary to establish sophisticated and robust attack detection techniques to defend against security threats as the use of drones becomes increasingly widespread and their applications diversify. This is due to the lack of privacy and security consideration in the drone’s system, including an inadequate computation capability and unsecured wireless channels to perform advanced cryptographic algorithms. Intrusion detection systems (IDS) and anomaly detection systems can identify suspicious activities and monitor network traffic, such as anomalous communication patterns or unauthorized access attempts. Therefore, the study presents an enhanced dwarf mongoose optimization algorithm with deep learning-based attack detection (EDMOA-DLAD) in Networks B5G for the purpose of Drones technique. The presented EDMOA-DLAD technique aims to recognize the attacks and classifies them on the drone network B5G. Primarily, the EDMOA-DLAD technique designs a feature selection (FS) approach using EDMOA. To detect attacks, the EDMOA-DLAD technique uses a deep variational autoencoder (DVAE) classifier. Finally, the EDMOA-DLAD technique applies the beetle antenna search (BAS) technique for the optimum hyperparameter part of DVAE model. The outcome of EDMOA-DLAD approach can be verified on benchmark datasets. A wide range of simulations inferred that the EDMOA-DLAD method obtains enhanced performance of 99.79% over other classification techniques.

IoT Security in Smart Cities

IoT Security in Smart Cities

Advanced connected autonomous vehicle (ACAV) with security and system design aspects for intelligent smart cities

Advanced connected autonomous vehicle (ACAV) with security and system design aspects for intelligent smart cities

Secure, Sustainable Smart Cities and the Internet of Things: Perspectives, Challenges, and Future Directions

A secure smart city integrates advanced technologies to enhance efficiency, sustainability, and safety while safeguarding citizens’ privacy and data [...]

Developing Smart City security through Upgrade Internet of Things Layers security

Developing Smart City security through Upgrade Internet of Things Layers security

Horizoning recent trends in the security of smart cities: Exploratory analysis using latent semantic analysis

The expeditious advancement and widespread implementation of intelligent urban infrastructure have yielded manifold advantages, albeit concurrently engendering novel security predicaments. Examining current patterns in the security of smart cities is paramount in comprehending nascent risks and formulating efficacious preventative measures. The present study suggests the utilization of Latent Semantic Analysis (LSA) as a means to scrutinize and reveal implicit semantic associations within a collection of textual materials pertaining to the security of smart cities. Through the process of gathering and pre-processing pertinent textual data, constructing a matrix that represents the frequency of terms within documents, and utilizing techniques that reduce the number of dimensions, Latent Semantic Analysis (LSA) has the ability to uncover concealed patterns and associations among concepts related to security. This study proposes five recommendations for future research that employ a topic modeling technique to investigate the often-explored subjects related to smart city security. This discovery provides additional evidence in favor of the proposition that a robust blockchain-driven framework is vital for the advancement of smart cities. Latent Semantic Analysis (LSA) offers important insights into the dynamic landscape of smart city security by employing several techniques such as pattern recognition, document or phrase clustering, and result visualization. Through the examination of patterns and developments, individuals in positions of political authority, urban planning, and security knowledge possess the ability to uphold their proficiency, render judicious choices substantiated by empirical data, and establish proactive strategies aimed at preserving the security, privacy, and sustainability of intelligent urban environments.