Author: Hewa Majeed Zangana, Senny Luckyardi, Firas Mahmood Mustafa, Shuai Li
DOI: 10.4018/979-8-3373-3296-3.ch010
Abstract
The rapid digital transformation of agriculture through smart farming technologies has introduced newcybersecurity challenges that threaten the integrity, confidentiality, and availability of critical agricultural data and systems. As precision agriculture, Internet of Things (IoT)-enabled sensors, and automated decision-making become integral to modern farming, the risks associated with cyber threats-such as data breaches, ransomware attacks, and supply chain vulnerabilities-continue to escalate. Unlike traditional security measures, AI-driven solutions, includingdeep learning and Large Language Models (LLMs), offer real-time threat detection, adaptive defense mechanisms, and enhanced risk assessment capabilities. This chapter explores the application of these technologies in securing agricultural networks, from intrusion detection to automated incident response. It also presents case studies of AI-driven cybersecurity solutions implemented in agricultural environments. © 2025 by IGI Global Scientific Publishing.
Keywords: Agricultural robots; Artificial life; Computer viruses
This article can be accessed at https://www.scopus.com/pages/publications/105004786381