Blockchain Integration with Machine Learning for Securing Fog Computing Vulnerability in Smart City Sustainability

Ajao, Lukman Adewale; Apeh, Simon T.

doi:10.1109/icaisc56366.2023.10085192

Cited by 8 publications

(2 citation statements)

References 22 publications

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“…For instance, Ref. [27] discusses the vulnerabilities of Smart City infrastructure to cyberattacks due to the use of IPv6 addressing and 5G networks. It proposes a security framework combining blockchain and machine learning to address these vulnerabilities, focusing on securing the fog computing layer.…”

Section: Related Workmentioning

confidence: 99%

Smart City as Cooperating Smart Areas: On the Way of Symbiotic Cyber–Physical Systems Environment

Tricomi,

Giacobbe,

Ficili

et al. 2024

Sensors

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The arising of the Cyber–Physical Systems’ vision and concepts drives technological evolution toward a new architectural design for the infrastructure of an environment referred to as a Smart Environment. This perspective alters the way systems within Smart City landscapes are conceived, designed, and ultimately realized. Modular architecture, resource-sharing techniques, and precise deployment approaches (such as microservices-oriented or reliant on the FaaS paradigm) serve as the cornerstones of a Smart City cognizant of multiple Cyber–Physical Systems composing it. This paper presents a framework integrating Digital Decisioning, encompassing the automated combination of human-derived knowledge and data-derived knowledge (e.g., business rules and machine learning), to enhance decision-making processes and application definition within the Smart City context.

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Section: Related Workmentioning

confidence: 99%

Smart City as Cooperating Smart Areas: On the Way of Symbiotic Cyber–Physical Systems Environment

Tricomi,

Giacobbe,

Ficili

et al. 2024

Sensors

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show abstract

“…The limitation of the study is that the analysis is based on an existing dataset, which may not cover all IoT security scenarios or provide a realistic assessment. Ajao and Apeh, 2023 [12] suggest an integrated architecture comprising of blockchain and machine learning for securing the fog computing layer vulnerability in smart city infrastructure. The study also discusses combining smart city-based Industrial Internet of Things (IIoT) with IPv6 addressing and 5G networks to enhance the Quality of Experience (QoE).…”

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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Ethereum blockchain-based three factor authentication and multi-contract access control for secure smart home environment in 5G networks

Atiewi,

Al-Rahayfeh,

Almiani

et al. 2023

Cluster Comput

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Blockchain Integration with Machine Learning for Securing Fog Computing Vulnerability in Smart City Sustainability

Cited by 8 publications

References 22 publications

Smart City as Cooperating Smart Areas: On the Way of Symbiotic Cyber–Physical Systems Environment

Smart City as Cooperating Smart Areas: On the Way of Symbiotic Cyber–Physical Systems Environment

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

Ethereum blockchain-based three factor authentication and multi-contract access control for secure smart home environment in 5G networks

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