CatBoost-based Intrusion Detection Method for the Physical Layer of Smart Agriculture

Abstract

Agriculture holds a pivotal role in the progress of human society. The challenges stemming from a burgeoning population, land degradation, water scarcity, and urbanization have intensified the need for more efficient agricultural production. While smart farming brings significant benefits to farmers and agricultural output, it also introduces complex cybersecurity risks to agricultural production. The security of the physical layer in smart agriculture is intricately tied to crop growth and yield, with indirect implications for the security of the network and application layers. This paper introduces a novel intrusion detection scheme based on CatBoost for the physical layer and evaluates its effectiveness using the publicly available ToN_IOT dataset. In binary classification results, the scheme achieves a remarkable recognition accuracy of 99.94%, along with a precision and recall of 99.88%. In multi-classification results, the scheme outperforms other existing solutions across all metrics. The experimental findings clearly illustrate the exceptional recognition accuracy of this implemented method against physical layer attacks within the domain of smart agriculture. Furthermore, the system’s implementation ensures the security of input data for the smart agriculture network layer, cloud, and blockchain applications.