Prediction of DDoS attacks in agriculture 4.0 with the help of prairie dog optimization algorithm with IDSNet
Abstract Integrating cutting-edge technology with conventional farming practices has been dubbed “smart agriculture” or “the agricultural internet of things.” Agriculture 4.0, made possible by the merging of Industry 4.0 and Intelligent Agriculture, is the next generation after industrial farming. A...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-09-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-42678-x |
| _version_ | 1797452699988393984 |
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| author | Ramesh Vatambeti D. Venkatesh Gowtham Mamidisetti Vijay Kumar Damera M. Manohar N. Sudhakar Yadav |
| author_facet | Ramesh Vatambeti D. Venkatesh Gowtham Mamidisetti Vijay Kumar Damera M. Manohar N. Sudhakar Yadav |
| author_sort | Ramesh Vatambeti |
| collection | DOAJ |
| description | Abstract Integrating cutting-edge technology with conventional farming practices has been dubbed “smart agriculture” or “the agricultural internet of things.” Agriculture 4.0, made possible by the merging of Industry 4.0 and Intelligent Agriculture, is the next generation after industrial farming. Agriculture 4.0 introduces several additional risks, but thousands of IoT devices are left vulnerable after deployment. Security investigators are working in this area to ensure the safety of the agricultural apparatus, which may launch several DDoS attacks to render a service inaccessible and then insert bogus data to convince us that the agricultural apparatus is secure when, in fact, it has been stolen. In this paper, we provide an IDS for DDoS attacks that is built on one-dimensional convolutional neural networks (IDSNet). We employed prairie dog optimization (PDO) to fine-tune the IDSNet training settings. The proposed model's efficiency is compared to those already in use using two newly published real-world traffic datasets, CIC-DDoS attacks. |
| first_indexed | 2024-03-09T15:12:26Z |
| format | Article |
| id | doaj.art-1a1f45810b664bc3aafee448cb736750 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T15:12:26Z |
| publishDate | 2023-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-1a1f45810b664bc3aafee448cb7367502023-11-26T13:16:11ZengNature PortfolioScientific Reports2045-23222023-09-0113111310.1038/s41598-023-42678-xPrediction of DDoS attacks in agriculture 4.0 with the help of prairie dog optimization algorithm with IDSNetRamesh Vatambeti0D. Venkatesh1Gowtham Mamidisetti2Vijay Kumar Damera3M. Manohar4N. Sudhakar Yadav5School of Computer Science and Engineering, VIT-AP UniversityDepartment of Computer Science and Engineering, GITAM School of Technology, GITAM University-Bengaluru CampusDepartment of Computer Science and Engineering, Malla Reddy UniversityDepartment of Computer Science and Engineering, Koneru Lakshmaiah Education FoundationDepartment of Computer Science and Engineering, CHRIST (Deemed to be University)Department of Information Technology, Chaitanya Bharathi Institute of TechnologyAbstract Integrating cutting-edge technology with conventional farming practices has been dubbed “smart agriculture” or “the agricultural internet of things.” Agriculture 4.0, made possible by the merging of Industry 4.0 and Intelligent Agriculture, is the next generation after industrial farming. Agriculture 4.0 introduces several additional risks, but thousands of IoT devices are left vulnerable after deployment. Security investigators are working in this area to ensure the safety of the agricultural apparatus, which may launch several DDoS attacks to render a service inaccessible and then insert bogus data to convince us that the agricultural apparatus is secure when, in fact, it has been stolen. In this paper, we provide an IDS for DDoS attacks that is built on one-dimensional convolutional neural networks (IDSNet). We employed prairie dog optimization (PDO) to fine-tune the IDSNet training settings. The proposed model's efficiency is compared to those already in use using two newly published real-world traffic datasets, CIC-DDoS attacks.https://doi.org/10.1038/s41598-023-42678-x |
| spellingShingle | Ramesh Vatambeti D. Venkatesh Gowtham Mamidisetti Vijay Kumar Damera M. Manohar N. Sudhakar Yadav Prediction of DDoS attacks in agriculture 4.0 with the help of prairie dog optimization algorithm with IDSNet Scientific Reports |
| title | Prediction of DDoS attacks in agriculture 4.0 with the help of prairie dog optimization algorithm with IDSNet |
| title_full | Prediction of DDoS attacks in agriculture 4.0 with the help of prairie dog optimization algorithm with IDSNet |
| title_fullStr | Prediction of DDoS attacks in agriculture 4.0 with the help of prairie dog optimization algorithm with IDSNet |
| title_full_unstemmed | Prediction of DDoS attacks in agriculture 4.0 with the help of prairie dog optimization algorithm with IDSNet |
| title_short | Prediction of DDoS attacks in agriculture 4.0 with the help of prairie dog optimization algorithm with IDSNet |
| title_sort | prediction of ddos attacks in agriculture 4 0 with the help of prairie dog optimization algorithm with idsnet |
| url | https://doi.org/10.1038/s41598-023-42678-x |
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