Efficient adaptive deep neural network model for securing demand side management in IoT enabled smart grid

Sivarajan, S.; Jebaseelan, S. D. Sundarsingh

doi:10.1016/j.ref.2022.08.003

Cited by 6 publications

(3 citation statements)

References 25 publications

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“…In field of machine learning, WSN node localization issue is regarded as a classification or multivariate regression problem. To address node localization issues in WSN-IoT, SVM classification [7] or SVM regression method [8] methods are used. Correlation techniques and the Bayesian learning methodology are used to address security challenges, as shown in [9].…”

Section: Related Workmentioning

confidence: 99%

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Smart Grid Security Based on Blockchain with Industrial Fault Detection Using Wireless Sensor Network and Deep Learning Techniques

Kandasamy,

Anto,

Baranitharan

et al. 2023

Journal of Sensors

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Low-cost monitoring and automation solutions for smart grids have been made viable by recent advancements in embedded systems and wireless sensor networks (W.S.N.s). A well-designed smart network of subsystems and metasystems known as a “smart grid” is aimed at enhancing the conventional power grid’s efficiency and guaranteeing dependable energy delivery. A smart grid (S.G.) requires two-way communication between utility providers and end users in order to accomplish its aims. This research proposes a novel technique in enhancing the smart grid security and industry fault detection using a wireless sensor network with deep learning architectures. The smart grid network security has been enhanced using a blockchain-based smart grid node routing protocol with IoT module. The industrial analysis has been carried out based on monitoring for fault detection in a network using Q-learning-based transfer convolutional network. The experimental analysis has been carried out in terms of bit error rate, end-end delay, throughput rate, spectral efficiency, accuracy, M.A.P., and RMSE. The proposed technique attained bit error rate of 65%, end-end delay of 57%, throughput rate of 97%, spectral efficiency of 93%, accuracy of 95%, M.A.P. of 55%, and RMSE of 75%. This proposed paradigm is advantageous for the operation of smart grids for increased security and industrial fault detection across the network because security is the biggest barrier in smart grid implementation.

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Section: Related Workmentioning

confidence: 99%

“…The denominator can only show a maximum value of 1.When choosing the best CH, the network delay must be minimised, and all cluster members are immediately affected. The network delay increases by equation (7) if the number of cluster members rises.…”

Section: R E T R a C T E Dmentioning

confidence: 99%

Smart Grid Security Based on Blockchain with Industrial Fault Detection Using Wireless Sensor Network and Deep Learning Techniques

Kandasamy,

Anto,

Baranitharan

et al. 2023

Journal of Sensors

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“…The proposed method was tested on the DS2Os dataset in the presence of sample attacks. Similarly, an effective and adaptable DNN was presented by [98] to protect smart grids, particularly for demand-side management, from cyberattacks. The proposed hybrid model combines DNN and a search technique called the Squirrel Search Algorithm (SSA).…”

Section: Machine Learning-based Solutionsmentioning

confidence: 99%

A Survey on Security, Privacy, Trust, and Architectural Challenges in IoT Systems

Adam,

Hammoudeh,

Alrawashdeh

et al. 2024

IEEE Access

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The Internet of Things (IoT) emerged as a pervasive technology, facilitating the seamless interaction of devices, individuals, and services, enabling data exchange and task execution across various domains. While the impact of IoT is undeniably transformative, its extensive proliferation raised significant concerns surrounding security, privacy, and trust, which stand as critical barriers to the widespread adoption and advancement of IoT technology. This review article explores IoT security, privacy, and trust research using a 3-layer IoT architecture. After introducing the fundamental tenets of IoT security, privacy, and trust, it proceeds to examine the prevalent security requirements within IoT architectures and their associated challenges. Then, the survey investigates the recent trends in research dedicated to addressing security, privacy, and trust issues within IoT systems. Furthermore, this article reviews the latest advancements and methodologies designed to secure IoT systems against security breaches and protect the privacy of sensitive data. Finally, the survey outlines unresolved challenges within the IoT security landscape and potential solutions. By offering this consolidated insight, this article offers a bridge between foundational and advanced IoT security topics, providing researchers with an in-depth understanding of current IoT security, privacy, and trust challenges, as well as cutting-edge solutions tailored to address the security and trust-related obstacles faced by IoT applications. This comprehensive overview equips the IoT community with the knowledge necessary to navigate the complex terrain of security, privacy, and trust in IoT systems.

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Industrial manufacturing process based on smart grid data classification with security using deep learning technique

Reddy¹,

Abdulkader

Reegu

et al. 2023

Int J Adv Manuf Technol

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Efficient adaptive deep neural network model for securing demand side management in IoT enabled smart grid

Cited by 6 publications

References 25 publications

Smart Grid Security Based on Blockchain with Industrial Fault Detection Using Wireless Sensor Network and Deep Learning Techniques

Smart Grid Security Based on Blockchain with Industrial Fault Detection Using Wireless Sensor Network and Deep Learning Techniques

A Survey on Security, Privacy, Trust, and Architectural Challenges in IoT Systems

Industrial manufacturing process based on smart grid data classification with security using deep learning technique

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