Network Threat Detection Mechanism for IoT-Based Precision Farming Using Machine Learning Techniques

Abstract

The rapid use of IoT in precision farming has transformed agricultural methods, enhancing efficiency and allowing for data-driven operations. Internet of Things (IoT) networks in precision agriculture are susceptible to a range of cyber-attacks, which put at risk both agricultural data and existing infrastructure. This study presents a network threat detection system that use machine learning mechanisms to protect precision agricultural systems based on the Internet of Things (IoT). Our methodology is based on a complex machine learning architecture that identifies anomalous traffic patterns that show security risks. Our anomaly detection system utilises classification methods like Random Forest, Support Vector Machine (SVM), and Neural Networks. The suggested approach was verified by utilising real-time Internet of Things (IoT) data obtained from a precision farming system. Findings show a substantial improvement in identifying different forms of assaults with a high level of precision and little occurrence of incorrect identifications, therefore establishing this approach as a very efficient method for improving IoT security in the field of agriculture.