Cyber Security for Detecting Distributed Denial of Service Attacks in Agriculture 4.0: Deep Learning Model

Abstract

Attackers are increasingly targeting Internet of Things (IoT) networks, which connect industrial devices to the Internet. To construct network intrusion detection systems (NIDSs), which can secure Agriculture 4.0 networks, powerful deep learning (DL) models have recently been deployed. An effective and adaptable intrusion detection system may be implemented by using the architectures of long short-term memory (LSTM) and convolutional neural network combined with long short-term memory (CNN–LSTM) for detecting DDoS attacks. The CIC-DDoS2019 dataset was used to design a proposal for detecting different types of DDoS attacks. The dataset was developed using the CICFlowMeter-V3 network. The standard network traffic dataset, including NetBIOS, Portmap, Syn, UDPLag, UDP, and normal benign packets, was used to test the development of deep learning approaches. Precision, recall, F1-score, and accuracy were among the measures used to assess the model’s performance. The suggested technology was able to reach a high degree of precision (100%). The CNN–LSTM has a score of 100% with respect to all the evaluation metrics. We used a deep learning method to build our model and compare it to existing systems to determine how well it performs. In addition, we believe that this proposed model has highest possible levels of protection against any cyber threat to Agriculture 4.0.