Machine Learning for Cybersecurity Frameworks in Smart Farming

In recent years, the rapid advancements in Artificial Intelligence (AI) have stimulated numerous breakthroughs and applications. With the advent of Industry 4.0, agriculture has become a primary subject of ongoing digitalization efforts. Modern agricultural applications prominently feature Decision Support Systems (DSS) characterized by practical User Interfaces (UI). While several applications align with these criteria, a noticeable gap exists in the domain of cybersecurity for smart farming. This paper addresses the identified gap by introducing an innovative solution: a robust tool designed to address critical security issues, including privacy, confidentiality, integrity, and availability. In this manner, the regular operation of the intelligent system is ensured, rendering it resilient against a diverse set of potential attack methods, including Denial of Service (DoS) attacks, replay attacks, and trojan horse attacks. Throughout this article, we articulate the systematic development of our framework, emphasizing its dependability and user-friendly attributes. By prioritizing cybersecurity, our framework contributes to establishing more resilient Information Technology infrastructures for the evolving landscape of modern agriculture.