GAN Base feedback analysis system for industrial IOT networks
The internet, like automated tools, has grown to better our daily lives. Interacting IoT products and cyber-physical systems. Generative Adversarial Network's (GANs') generator and discriminator may have different inputs, allowing feedback in supervised models. AI systems use neural networ...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-04-01
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| Series: | Automatika |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/00051144.2022.2140391 |
| _version_ | 1811177006284406784 |
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| author | K. Ashok Rajasekhar Boddu Salman Ali Syed Vijay R. Sonawane Ravindra G. Dabhade Pundru Chandra Shaker Reddy |
| author_facet | K. Ashok Rajasekhar Boddu Salman Ali Syed Vijay R. Sonawane Ravindra G. Dabhade Pundru Chandra Shaker Reddy |
| author_sort | K. Ashok |
| collection | DOAJ |
| description | The internet, like automated tools, has grown to better our daily lives. Interacting IoT products and cyber-physical systems. Generative Adversarial Network's (GANs') generator and discriminator may have different inputs, allowing feedback in supervised models. AI systems use neural networks, and adversarial networks analyse neural network feedback. Cyber-physical production systems (CPPS) herald intelligent manufacturing . CPPS may launch cross-domain attacks since the virtual and real worlds are interwoven. This project addresses enhanced Cyber-Physical System(CPS) feedback structure for Denial-of-Service (DoS) defence . Comparing sensor-controller and controller-to-actuator DoS attack channels shows a swapping system modelling solution for the CPS's complex response feedback. Because of the differential in bandwidth between the two channels and the suspects' limited energy, one person can only launch so many DoS assaults. DoS attacks are old and widespread. Create a layered switching paradigm that employs packet-based transfer techniques to prevent assaults. The discriminator's probability may be used to assess whether feedback samples came from real or fictional data. Cognitive feedback can assess GA feedback data. |
| first_indexed | 2024-04-10T20:01:17Z |
| format | Article |
| id | doaj.art-472dd0e130254e33bb15cb210f0ea984 |
| institution | Directory Open Access Journal |
| issn | 0005-1144 1848-3380 |
| language | English |
| last_indexed | 2024-04-10T20:01:17Z |
| publishDate | 2023-04-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Automatika |
| spelling | doaj.art-472dd0e130254e33bb15cb210f0ea9842023-01-27T04:35:05ZengTaylor & Francis GroupAutomatika0005-11441848-33802023-04-0164225926710.1080/00051144.2022.2140391GAN Base feedback analysis system for industrial IOT networksK. Ashok0Rajasekhar Boddu1Salman Ali Syed2Vijay R. Sonawane3Ravindra G. Dabhade4Pundru Chandra Shaker Reddy5Department of Computer Science and Engineering, New Horizon College of Engineering, Bangalore, IndiaDepartment of Software Engineering, College of Computing and Informatics, Haramaya University, Dire Dawa, EthiopiaDepartment of Computer Science, Applied College, Jouf University, Tabarjal, Kingdom of Saudi ArabiaDepartment of Information Technology, MVPS's Karmaveer Adv.Baburao Ganpatrao Thakare College of Engineering, Nashik, IndiaDepartment of Electronics & Telecommunication Engineering, Matoshri College of Engineering & Research Centre, Nashik, IndiaSchool of Computing and Information Technology, REVA University, Bangalore, IndiaThe internet, like automated tools, has grown to better our daily lives. Interacting IoT products and cyber-physical systems. Generative Adversarial Network's (GANs') generator and discriminator may have different inputs, allowing feedback in supervised models. AI systems use neural networks, and adversarial networks analyse neural network feedback. Cyber-physical production systems (CPPS) herald intelligent manufacturing . CPPS may launch cross-domain attacks since the virtual and real worlds are interwoven. This project addresses enhanced Cyber-Physical System(CPS) feedback structure for Denial-of-Service (DoS) defence . Comparing sensor-controller and controller-to-actuator DoS attack channels shows a swapping system modelling solution for the CPS's complex response feedback. Because of the differential in bandwidth between the two channels and the suspects' limited energy, one person can only launch so many DoS assaults. DoS attacks are old and widespread. Create a layered switching paradigm that employs packet-based transfer techniques to prevent assaults. The discriminator's probability may be used to assess whether feedback samples came from real or fictional data. Cognitive feedback can assess GA feedback data.https://www.tandfonline.com/doi/10.1080/00051144.2022.2140391Denial-of-Servicecyber-physical production systems (CPPS)cognitive feedbackgenerative adversarial networks (GANs) |
| spellingShingle | K. Ashok Rajasekhar Boddu Salman Ali Syed Vijay R. Sonawane Ravindra G. Dabhade Pundru Chandra Shaker Reddy GAN Base feedback analysis system for industrial IOT networks Automatika Denial-of-Service cyber-physical production systems (CPPS) cognitive feedback generative adversarial networks (GANs) |
| title | GAN Base feedback analysis system for industrial IOT networks |
| title_full | GAN Base feedback analysis system for industrial IOT networks |
| title_fullStr | GAN Base feedback analysis system for industrial IOT networks |
| title_full_unstemmed | GAN Base feedback analysis system for industrial IOT networks |
| title_short | GAN Base feedback analysis system for industrial IOT networks |
| title_sort | gan base feedback analysis system for industrial iot networks |
| topic | Denial-of-Service cyber-physical production systems (CPPS) cognitive feedback generative adversarial networks (GANs) |
| url | https://www.tandfonline.com/doi/10.1080/00051144.2022.2140391 |
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