外文摘要：The Internet of Things (IoT) tunes modern technologies, including wireless sensors and cloud computing, to create a homogeneous and highly effective environment. Therefore, IoT has emerged in various fields of life, such as healthcare, industry, and agriculture. Agriculture is among the primary components of developing nations' financial states and is vital in maintaining human life. However, the human capacity to reproduce far exceeds the capability of our planet to secure the food required for our lives. Hence, the emergence of IoT in this industry has seen essential advancements to help boost agriculture production and quality. In addition, this emergence exposes the smart agriculture environment to considerable cyber threats. This paper presents a network intrusion detection system (NIDS) to mitigate smart agriculture security vulnerabilities. We developed our framework using radial basis functions neural networks (RBFNN) to detect and classify intrusions in the IoT network. To get our model to perform in its best form, we applied crowd wisdom tree-based machine learning (ML) techniques to select relevant features from the datasets, such as random Forrest (RF), AdaBoost (ADA), extra trees (ET), LightGBM (LGBM), and XGBoost (XGB). We implemented a single-class support vector machine (1-CSVM) to detect and remove outliers. We evaluated our model using NF-Bot-IoT and NF-ToN-IoT datasets. It scored 99.25% accuracy (ACC) and 82.97% Matthews correlation coefficient (MCC) and 90.05% MCC and 96.92% ACC on preprocessed NF-Bot-IoT and NF-ToN-IoT, respectively. Our model showed outstanding performance in overcoming the NF-Bot-IoT dataset imbalance.
外文关键词：feature selection;artificial neural network;intrusion detection;Smart farming security
作者：Mohy-eddine, Mouaad;Guezzaz, Azidine;Benkirane, Said;Azrour, Mourade
作者单位：Cadi Ayyad Univ;Moulay Ismail Univ Meknes
期刊名称：CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
期刊影响因子：0.0
出版年份：2024
出版刊次：27(6)
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台