Application of Artificial Intelligence and Agromatics in the Cybersecurity of Agricultural Infrastructures in Regional Contexts
DOI:
https://doi.org/10.70577/asce.v5i1.675Keywords:
Digital agriculture, Cybersecurity, Agricultural Information systems, Data protection, Rural digital transformationAbstract
The increasing digitalization of the agricultural sector has intensified dependence on intelligent infrastructures based on IoT, edge computing, and artificial intelligence, transforming traditional production systems into interconnected agro-digital ecosystems. However, this technological evolution significantly expands the exposure surface to cyber threats, particularly in rural environments where infrastructure and digital governance present structural limitations. This study proposes and validates a hybrid architecture based on deep learning models, edge computing, and decentralized integrity mechanisms to strengthen cybersecurity in regional agricultural infrastructures. A simulated environment (testbed) was developed integrating agricultural IoT sensors and critical networks, evaluating CNN–LSTM models and autoencoders trained with the CIC-DDoS2019 and TON-IoT datasets. The results demonstrate detection levels exceeding 98%, with latency values suitable for operation in environments with limited connectivity. The findings confirm that integrating artificial intelligence at the edge enhances operational resilience and reduces reliance on centralized infrastructure, which is strategically relevant for rural regions undergoing digital transformation. It is concluded that cybersecurity must be incorporated as a structural component of smart agriculture, articulating technological innovation, data governance, and sustainable development.
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