Real-time Cybersecurity Situational Awareness Framework for Agriculture Machinery-based IoT Networks

The growing integration of the Internet of Things (IoT), cloud computing, and artificial intelligence (AI) in modern agriculture machinery has revolutionized the food and agriculture sector by significantly improving the efficiency and precision of farming equipment. Global agriculture machinery manufacturers have started adopting and implementing advanced cloud-based platforms and technologies for monitoring machinery and equipment performance and enabling remote diagnostics and updates. However, these platforms largely focus on functional analytics and fleet management, often overlooking critical cybersecurity considerations. As agriculture machinery increasingly relies on interconnected systems, including in-vehicle networks such as Controller Area Networks (CAN), edge computing, and cloud services, the attack surface continues to expand, making cybersecurity an essential component of situational awareness. This paper proposes a real-time 2-tier cybersecurity situational awareness framework for agriculture machinery-based IoT networks (Ag-CSA). The proposed Ag-CSA framework combines an edge-based machine learning anomaly detection system (MLADS) with a cloud-based alert correlation to identify, aggregate, and analyze cyber threats. The framework is validated within an experimental testbed environment using CAN-specific datasets, achieving low prediction latency (<10 ms), high alert confidence (≈84%), and low end-to-end system latency (<400 ms).