Securing Industry 4.0-Based Precision Agriculture
Industry 4.0 presents a novel dimension in the domain of smart agriculture of the tardiest decade. In precision agriculture, the on-field sensor node receives the repetitive agriculture data and forwards it to the isolated station across the wireless carriers. Data received at the base station is big therefore they are deposited in the cloud storage. Cloud computing interfaces have been built utilizing the HTTP protocol, and therefore different HTTP threats have presented to harm agriculture data. Several techniques have been proposed such as threat detection with shortcomings of less precision and higher detection failures. Thus implementing such conventional methods for attack detection is a nightmare and needed an effective method to detect such cyber threats for the Industry 4.0-connected cloud computing interface. The proposed work presents the methodology of detecting the HTTP threats in the cloud storage connected with Industry 4.0-based precision agriculture applications. The results confirmed that the proposed technique obtained better performance compared to state-of-art methods for HTTP attack detection in cloud computing applications.Reducing the use of water, composts, herbicides, and pesticides outside of farmhouse kinds of equipment is one way that precision farming may provide practical and ecological benefits.Social event data, data execution, and control advancements in precision agriculture all rely on WSN. The reliability and robustness of WSNs, which have had a significant influence via the wireless channel, are absolutely critical to the effectiveness of such an intelligent framework.Precision Security: Before investigating the challenges of precision farming routing, we first focused on the detection of Hypertext Transport Protocol (HTTP) threats under Industry 4.0-connected methods. We developed a cloud-connected precision farming application & applied the data mining & machine learning algorithms for accurate HTTP threats. This contribution addresses the security challenge for the precision farming experiment.
Authors
- Rajesh Keshavrao Deshmukh
- Aparna A. Junnarkar (ORCID: https://orcid.org/0000-0002-3187-9228)
- Ashish Bapurao Manwatkar
- Shubhangi Jagdish Kamble
- Pranita Kishor Kachare
- Snehal Prashant Latkar
Institutions
- International Institute of Information Technology (IN)
- Kalinga University (IN)
Publication Details
- Published
- 2024-12-13
- DOI
- https://doi.org/10.1109/ictbig64922.2024.10911640
- Citations
- 1
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 0.60