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.

Details

Title
Prediction of DDoS attacks in agriculture 4.0 with the help of prairie dog optimization algorithm with IDSNet
Author
Vatambeti, Ramesh 1 ; Venkatesh, D. 2 ; Mamidisetti, Gowtham 3 ; Damera, Vijay Kumar 4 ; Manohar, M. 5 ; Yadav, N. Sudhakar 6 

 VIT-AP University, School of Computer Science and Engineering, Vijayawada, India (GRID:grid.513382.e) (ISNI:0000 0004 7667 4992) 
 GITAM School of Technology, GITAM University-Bengaluru Campus, Department of Computer Science and Engineering, Bengaluru, India (GRID:grid.411710.2) (ISNI:0000 0004 0497 3037) 
 Malla Reddy University, Department of Computer Science and Engineering, Hyderabad, India (GRID:grid.411710.2) 
 Koneru Lakshmaiah Education Foundation, Department of Computer Science and Engineering, Hyderabad, India (GRID:grid.449504.8) (ISNI:0000 0004 1766 2457) 
 CHRIST (Deemed to be University), Department of Computer Science and Engineering, Bangalore, India (GRID:grid.440672.3) (ISNI:0000 0004 1761 0390) 
 Chaitanya Bharathi Institute of Technology, Department of Information Technology, Hyderabad, India (GRID:grid.454281.e) (ISNI:0000 0004 1772 4312) 
Pages
15371
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2865420230
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.