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Deep Learning-Based Intrusion Detection for Distributed Denial of Service Attack in Agriculture 4.0 - 2021

Research Area:  Machine Learning

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 models 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.

Author(s) Name:  Mohamed Amine Ferrag, Lei Shu, Hamouda Djallel and Kim-Kwang Raymond Choo

Journal name:   Electronics

Conferrence name:  

Publisher name:  MDPI

DOI:  10.3390/electronics10111257

Volume Information:  Volume 10 Issue 11