Detection of DDoS attacks in SDN switches with deep learning and swarm intelligence approach
Mohsen Eghbali
1
(
Department of Computer Engineering, Maybod Branch, Islamic Azad University, Maybod, Iran
)
Mohammadreza Mollkhalili Maybodi
2
(
Associate Professor, Department of Computer Engineering, Maybod Branch, Islamic Azad University, Maybod, Iran
)
Keywords: Internet of Things, DDoS attacks, deep learning, Intrusion Detection System, SDN network,
Abstract :
Internet of Things nodes infected with various types of malware and any smart device can appear as a botnet attacking node. The challenge of most intrusion detection systems in the Internet of Things is the need for intelligent feature selection and the imbalance of the training data set and centralization. In this article, an efficient intrusion detection system for the Internet of Things based on the distributed architecture of the SDN network is presented. In the proposed method, the data set is balanced using the SMOTE method in the first stage. Then in the second stage, the essential features are selected using the African vulture optimization algorithm. In the third step, the LSTM deep learning method is trained in the SDN controller so that the switches of the SDN network use this trained model to detect attacks. In the proposed method, the addresses of attacking nodes are shared between SDN switches so that the attacking node is recognized as an attacking node in all switches and DDoS attacks are stopped. Experiments running in the MATLAB environment and the NSL-KDD dataset and the results of the experiments show that the proposed method in detecting attacks has accuracy, sensitivity, and precision of 99.34%, 99.16%, and 98.93%. The proposed method is more accurate in detecting DDoS attacks than the feature selection methods based on WOA, HHO, and AO algorithms. The proposed method for detecting DDoS attacks is more accurate than deep learning methods such as LSTM, RNN, and CNN.
