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Big Data Mining and Analytics 2024, 7(2): 399-418 https://doi.org/10.26599/BDMA.2023.9020022
Open Access | Issue | Published: 22 April 2024

An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems

Show Author's Information Sankaramoorthy Muthubalaji1,  Naresh Kumar Muniyaraj2,  Sarvade Pedda Venkata Subba Rao3,  Kavitha Thandapani4,  Pasupuleti Rama Mohan5,  Thangam Somasundaram6,  Yousef Farhaoui7( )
Department of Electrical and Electronics Engineering, CMR College of Engineering & Technology, Hyderabad 501401, India
Department of Electronics and Communication Engineering, Vardhaman College of Engineering Kacharam, Shamshabad 501218, India
Department of Electronics and Communication Engineering, Sreenidhi Institute of Science and Technology, Hyderabad 501301, India
Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai 600062, India
Department of Electrical and Electronics Engineering, Bharat Institute of Engineering and Technology, Hyderabad 501510, India
Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidhyapeetham, Bengaluru 560035, India
T-IDMS, Department of Computer Science, Faculty of Sciences and Techniques, Moulay Ismail University, Errachidia 52000, Morocco
Keywords:
smart grid, Machine Learning (ML), big data analytics, AdaBelief Exponential Feature Selection (AEFS), Polar Bear Optimization (PBO), Kernel Extreme Neural Network (KENN)
Cite this article:
Muthubalaji S, Kumar Muniyaraj N, Pedda Venkata Subba Rao S, et al. An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems. Big Data Mining and Analytics, 2024, 7(2): 399-418. https://doi.org/10.26599/BDMA.2023.9020022
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Abstract Full text About this article

Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid, which also supports to obtain a variety of technological, social, and financial benefits. There is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies, along with data processing and advanced tools. The main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information security. The original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI mechanisms. Here, an AdaBelief Exponential Feature Selection (AEFS) technique is used to efficiently handle the input huge datasets from the smart grid for boosting security. Then, a Kernel based Extreme Neural Network (KENN) technique is used to anticipate security vulnerabilities more effectively. The Polar Bear Optimization (PBO) algorithm is used to efficiently determine the parameters for the estimate of radial basis function. Moreover, several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection- Kernel based Extreme Neural Network (AEFS-KENN) big data security framework. The results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5% with precision and AUC of 99% for all smart grid big datasets used in this study.


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About this article

An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems

Department of Electrical and Electronics Engineering, CMR College of Engineering & Technology, Hyderabad 501401, India
Department of Electronics and Communication Engineering, Vardhaman College of Engineering Kacharam, Shamshabad 501218, India
Department of Electronics and Communication Engineering, Sreenidhi Institute of Science and Technology, Hyderabad 501301, India
Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai 600062, India
Department of Electrical and Electronics Engineering, Bharat Institute of Engineering and Technology, Hyderabad 501510, India
Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidhyapeetham, Bengaluru 560035, India
T-IDMS, Department of Computer Science, Faculty of Sciences and Techniques, Moulay Ismail University, Errachidia 52000, Morocco

Abstract

Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid, which also supports to obtain a variety of technological, social, and financial benefits. There is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies, along with data processing and advanced tools. The main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information security. The original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI mechanisms. Here, an AdaBelief Exponential Feature Selection (AEFS) technique is used to efficiently handle the input huge datasets from the smart grid for boosting security. Then, a Kernel based Extreme Neural Network (KENN) technique is used to anticipate security vulnerabilities more effectively. The Polar Bear Optimization (PBO) algorithm is used to efficiently determine the parameters for the estimate of radial basis function. Moreover, several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection- Kernel based Extreme Neural Network (AEFS-KENN) big data security framework. The results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5% with precision and AUC of 99% for all smart grid big datasets used in this study.

Keywords: smart grid, Machine Learning (ML), big data analytics, AdaBelief Exponential Feature Selection (AEFS), Polar Bear Optimization (PBO), Kernel Extreme Neural Network (KENN)

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Received: 22 March 2023
Revised: 20 July 2023
Accepted: 06 August 2023
Published: 22 April 2024
Issue date: June 2024

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