Secure edge computing vulnerabilities in smart cities sustainability using petri net and genetic algorithm-based reinforcement learning

Ajao, Lukman Adewale; Apeh, Simon T.

doi:10.1016/j.iswa.2023.200216

Cited by 9 publications

(3 citation statements)

References 40 publications

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“…Ajao et al [116] proposed a Blockchain-Based Machine Learning -Intrusion Detection System (BML-IDS) framework to protect networks for sustainable smart city fog computing. Data flow is detected and secured using blockchain and machine learning algorithms.…”

Section: ) Edge Computing and Fog Computingmentioning

confidence: 99%

Exploring the Potential of Blockchain Technology in an IoT-Enabled Environment: A Review

Deepak,

Gulia,

Gill

et al. 2024

IEEE Access

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Internet of Things (IoT) plays an essential contribution in connecting devices and enabling seamless data exchange, leading to increased efficiency and convenience. However, security concerns in IoT systems are significant, as compromised devices can lead to data breaches and privacy violations. Blockchain technology can enhance IoT security by providing decentralized consensus, immutability, and transparent transaction records, ensuring secure and trustworthy communication and data integrity. This review article gives a succinct but thorough understanding of blockchain technology, covering architecture of blockchain, working principles, types, applications, platforms, and its role in the IoT environment. The study highlights potential benefits of blockchain like enhanced security and privacy, and explores its integration with IoT. Additionally, the study discusses various real-world applications, examines blockchain platforms, and addresses the limitations and challenges associated with blockchain technology. This review serves as a valuable resource for researchers and practitioners seeking a deeper understanding of blockchain's potential and its implications in the IoT landscape.INDEX TERMS Blockchain, Data Exchange, Internet of Things (IoT), Privacy, Security.

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Section: ) Edge Computing and Fog Computingmentioning

confidence: 99%

Exploring the Potential of Blockchain Technology in an IoT-Enabled Environment: A Review

Deepak,

Gulia,

Gill

et al. 2024

IEEE Access

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“…Ajao and Apeh, 2023 [5] introduce a Petri net-genetic algorithm-based reinforcement learning (GARL) technique to address security issues in smart cities' Industrial Internet of Things (IIoT) networks. The framework includes a trust model and distributed authorization for information control, achieving high anomaly detection rates.…”

Section: Related Workmentioning

confidence: 99%

Anomaly Detection of IoT Cyberattacks in Smart Cities Using Federated Learning and Split Learning

Priyadarshini

2024

BDCC

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The swift proliferation of the Internet of Things (IoT) devices in smart city infrastructures has created an urgent demand for robust cybersecurity measures. These devices are susceptible to various cyberattacks that can jeopardize the security and functionality of urban systems. This research presents an innovative approach to identifying anomalies caused by IoT cyberattacks in smart cities. The proposed method harnesses federated and split learning and addresses the dual challenge of enhancing IoT network security while preserving data privacy. This study conducts extensive experiments using authentic datasets from smart cities. To compare the performance of classical machine learning algorithms and deep learning models for detecting anomalies, model effectiveness is assessed using precision, recall, F-1 score, accuracy, and training/deployment time. The findings demonstrate that federated learning and split learning have the potential to balance data privacy concerns with competitive performance, providing robust solutions for detecting IoT cyberattacks. This study contributes to the ongoing discussion about securing IoT deployments in urban settings. It lays the groundwork for scalable and privacy-conscious cybersecurity strategies. The results underscore the vital role of these techniques in fortifying smart cities and promoting the development of adaptable and resilient cybersecurity measures in the IoT era.

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“…Threat Prevention: Threat prevention involves taking proactive measures to prevent cyber threats from becoming successful attacks [23]. This may include blocking unauthorized access, strengthening security, and taking preventive measures to reduce risks.…”

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confidence: 99%

IoT Anomaly Detection to Strengthen Cybersecurity in the Critical Infrastructure of Smart Cities

Villegas-Ch,

Govea,

Jaramillo-Alcazar

2023

Applied Sciences

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This study addresses anomaly detection in smart city environments driven by the Internet of Things. In these cities, digital interconnection and the extensive network of sensors generate enormous amounts of data, which are essential to improving citizens’ efficiency and quality of life. However, this data may also contain strange events that require early detection to ensure the proper functioning of urban systems. For this, anomaly detection models are explored to identify unusual patterns in urban data. The work focuses on the applicability and effectiveness of these models in different urban scenarios supported by the Internet of Things. Furthermore, its performance is evaluated by comparing it with existing approaches, and its advantages and limitations are analyzed. The results show that the proposed models, including Isolation Forest, recurrent neural network, and variational autoencoder, are highly effective in detecting anomalies in urban data. This work contributes to the field of smart cities by improving the safety and efficiency of urban systems. Early detection of anomalies makes it possible to prevent unplanned interruptions, ensure the safety of citizens, and maintain the integrity of urban systems. Furthermore, the relevance of this work in the existing literature and its importance for the evolution of smart cities supported by the Internet of Things are highlighted.

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Secure edge computing vulnerabilities in smart cities sustainability using petri net and genetic algorithm-based reinforcement learning

Cited by 9 publications

References 40 publications

Exploring the Potential of Blockchain Technology in an IoT-Enabled Environment: A Review

Exploring the Potential of Blockchain Technology in an IoT-Enabled Environment: A Review

Anomaly Detection of IoT Cyberattacks in Smart Cities Using Federated Learning and Split Learning

IoT Anomaly Detection to Strengthen Cybersecurity in the Critical Infrastructure of Smart Cities

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