GMLP-IDS: A Novel Deep Learning-Based Intrusion Detection System for Smart Agriculture

Abstract

Smart Agriculture, also known as Agricultural 5.0, is expected to be an integral part of our human lives to reduce the cost of agricultural inputs, increasing productivity and improving the quality of the final product. Indeed, the safety and ongoing maintenance of Smart Agriculture from cyber-attacks are vitally important. To provide more comprehensive protection against potential cyber-attacks, this paper proposes a new deep learning-based intrusion detection system for securing Smart Agriculture. The proposed Intrusion Detection System IDS, namely GMLP-IDS, combines the feedforward neural network Multilayer Perceptron (MLP) and the Gaussian Mixture Model (GMM) that can better protect the Smart Agriculture system. GMLP-IDS is evaluated with the CIC-DDoS2019 dataset, which contains various Distributed Denial-of-Service (DDoS) attacks. The paper first uses the Pearson’s correlation coefficient approach to determine the correlation between the CIC-DDoS2019 dataset characteristics and their corresponding class labels. Then, the CIC-DDoS2019 dataset is divided randomly into two parts, i.e., training and testing. 75% of the data is used for training, and 25% is employed for testing. The performance of the newly proposed IDS has been compared to the traditional MLP model in terms of accuracy rating, loss rating, recall, and F1 score. Comparisons are handled on both binary and multi-class classification problems. The results revealed that the proposed GMLP-IDS system achieved more than 99.99% detection accuracy and a loss of 0.02% compared to traditional MLP. Furthermore, evaluation performance demonstrates that the proposed approach covers a more comprehensive range of security properties for Smart Agriculture and can be a promising solution for detecting unknown DDoS attacks.