An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems
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 tech...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2024-06-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2023.9020022 |
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| author | Sankaramoorthy Muthubalaji Naresh Kumar Muniyaraj Sarvade Pedda Venkata Subba Rao Kavitha Thandapani Pasupuleti Rama Mohan Thangam Somasundaram Yousef Farhaoui |
| author_facet | Sankaramoorthy Muthubalaji Naresh Kumar Muniyaraj Sarvade Pedda Venkata Subba Rao Kavitha Thandapani Pasupuleti Rama Mohan Thangam Somasundaram Yousef Farhaoui |
| author_sort | Sankaramoorthy Muthubalaji |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e0e092855a6e430ab001e5e031cb74cc |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-e0e092855a6e430ab001e5e031cb74cc2025-02-02T04:59:12ZengTsinghua University PressBig Data Mining and Analytics2096-06542024-06-017239941810.26599/BDMA.2023.9020022An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid SystemsSankaramoorthy Muthubalaji0Naresh Kumar Muniyaraj1Sarvade Pedda Venkata Subba Rao2Kavitha Thandapani3Pasupuleti Rama Mohan4Thangam Somasundaram5Yousef Farhaoui6Department of Electrical and Electronics Engineering, CMR College of Engineering & Technology, Hyderabad 501401, IndiaDepartment of Electronics and Communication Engineering, Vardhaman College of Engineering Kacharam, Shamshabad 501218, IndiaDepartment of Electronics and Communication Engineering, Sreenidhi Institute of Science and Technology, Hyderabad 501301, IndiaDepartment of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai 600062, IndiaDepartment of Electrical and Electronics Engineering, Bharat Institute of Engineering and Technology, Hyderabad 501510, IndiaDepartment of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidhyapeetham, Bengaluru 560035, IndiaT-IDMS, Department of Computer Science, Faculty of Sciences and Techniques, Moulay Ismail University, Errachidia 52000, MoroccoBig 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.https://www.sciopen.com/article/10.26599/BDMA.2023.9020022smart gridbig data analyticsmachine learning (ml)adabelief exponential feature selection (aefs)polar bear optimization (pbo)kernel extreme neural network (kenn) |
| spellingShingle | Sankaramoorthy Muthubalaji Naresh Kumar Muniyaraj Sarvade Pedda Venkata Subba Rao Kavitha Thandapani Pasupuleti Rama Mohan Thangam Somasundaram Yousef Farhaoui 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 smart grid big data analytics machine learning (ml) adabelief exponential feature selection (aefs) polar bear optimization (pbo) kernel extreme neural network (kenn) |
| title | An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems |
| title_full | An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems |
| title_fullStr | An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems |
| title_full_unstemmed | An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems |
| title_short | An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems |
| title_sort | intelligent big data security framework based on aefs kenn algorithms for the detection of cyber attacks from smart grid systems |
| topic | smart grid big data analytics machine learning (ml) adabelief exponential feature selection (aefs) polar bear optimization (pbo) kernel extreme neural network (kenn) |
| url | https://www.sciopen.com/article/10.26599/BDMA.2023.9020022 |
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