Abstract
Smart Agriculture or Agricultural Internet of things, consists of integrating advanced technologies (e.g., NFV, SDN, 5G/6G, Blockchain, IoT, Fog, Edge, and AI) into existing farm operations to improve the quality and productivity of agricultural products. The convergence of Industry 4.0 and Intelligent Agriculture provides new opportunities for migration from factory agriculture to the future generation, known as Agriculture 4.0. However, since the deployment of thousands of IoT based devices is in an open field, there are many new threats in Agriculture 4.0. Security researchers are involved in this topic to ensure the safety of the system since an adversary can initiate many cyber attacks, such as DDoS attacks to making a service unavailable and then injecting false data to tell us that the agricultural equipment is safe but in reality, it has been theft. In this paper, we propose a deep learning-based intrusion detection system for DDoS attacks based on three models, namely, convolutional neural networks, deep neural networks, and recurrent neural networks. Each model’s performance is studied within two classification types (binary and multiclass) using two new real traffic datasets, namely, CIC-DDoS2019 dataset and TON_IoT dataset, which contain different types of DDoS attacks.
Highlights
The 4th revolution of the industrial era is the new industry trend that defines the Smart Factory concept [1]
The results show that deep learning techniques can provide better performance in cyber security intrusion detection for Agriculture 4.0
The results show that deep learning techniques give a higher positive prediction in terms of binary classification compared to multiclass classification
Summary
The 4th revolution of the industrial era (or Industry 4.0) is the new industry trend that defines the Smart Factory concept [1] This concept is based on emerging technologies such as Fog computing, Cloud computing, Artificial Intelligence, Deep learning. This paper focuses on developing and employing deep-learning approaches for detecting cyber threats (i.e., anomaly-based IDS). There are some recently proposed IDS systems that employ deep learning strategies for IoT applications, such as wireless networks [10], big data environments [11], industrial cyber–physical systems [12], SCADA systems [13], smart grids [14], internet of vehicles [15], and cloud computing [16].
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