Deep Learning-Based Intrusion Detection for Distributed Denial of Service Attack in Agriculture 4.0

dc.contributor.authorFerrag, Mohamed Amine
dc.contributor.authorShu, Lei
dc.contributor.authorDjallel, Hamouda
dc.contributor.authorChoo, Kim-Kwang Raymond
dc.description.abstractSmart 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.
dc.description.departmentInformation Systems and Cyber Security
dc.identifierdoi: 10.3390/electronics10111257
dc.identifier.citationElectronics 10 (11): 1257 (2021)
dc.rightsAttribution 4.0 United States
dc.subjectdeep learning approaches
dc.subjectintrusion detection system
dc.subjectAgriculture 4.0
dc.subjectDDoS attack
dc.subjectsmart agriculture
dc.titleDeep Learning-Based Intrusion Detection for Distributed Denial of Service Attack in Agriculture 4.0


Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
1.24 MB
Adobe Portable Document Format

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
0 B
Item-specific license agreed upon to submission