Abstract

Smart environments such as environments for healthcare, industrial automation and agriculture systems have been significantly changed by the quick boom of the Internet of Things. The growing connectivity of IoT devices, however, raises the risks that networks face from cyber threats including distributed denial-of-service (DDoS), spoofing, ransomware and botnets. The sheer volume, diversity, and imbalance of network traffic data in dynamic IoT environments can be the cause of many traditional IDS solutions not achieving high detection accuracy and scalability. Machine learning and deep learning techniques have been found to be effective in enhancing IDS performance in recent studies, such as XGBoost and gradient boosting frameworks. A secure and intelligent intrusion detection system (IDS) for Internet of Things (IoT) in smart farming environment based on the optimized XGBoost and blockchain integration is proposed in this paper. The proposed framework integrates mechanisms for preprocessing, addressing imbalance, optimization of features, and mechanisms for providing explanations of AI to boost detection accuracy and understanding. It is built with blockchain to facilitate secure handling and communication of data between IoT devices. The proposed model is based on the recent developments made in optimized gradient boosting and explainable intrusion detection systems given recently in the literature. Through the comparative analysis, it is found that the framework is more precise, with lower false positive rates and is more secure for IoT network with limited resources. The study reveals that machine learning, explainable AI and blockchain technologies can be leveraged to develop a scalable and secure intrusion detection capability for next-generation smart agriculture system.

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