Voting extreme learning machine based distributed denial of service attack detection in cloud computing

Authors：Gopal Singh Kushwah; Virender Ranga

Source：Journal of Information Security and Applications, 2020, 53.

DOI：10.1016/j.jisa.2020.102532

• Cloud computing
• DDoS attacks
• Artificial neural networks
• Extreme learning machine

Distributed denial of service attack is one of the most dangerous attacks in cloud computing. This attack makes the cloud services inaccessible to the end-users by exhausting resources, resulting in heavy economic and reputation loss. So, developing defensive solutions against these attacks is necessary for the widespread adoption of cloud computing technology. This paper proposes a new system for detecting DDoS attacks in cloud computing environment. The proposed system is built using voting extreme learning machine (V-ELM), a type of artificial neural network. Experiments were performed to evaluate the performance of the proposed system by using two benchmark datasets viz. NSL-KDD dataset and ISCX intrusion detection dataset. It has been shown by experiments that the proposed system detects attacks with an accuracy of 99.18% with the NSL-KDD dataset and 92.11% with the ISCX dataset. Performance of the proposed system has been compared with other systems based on backpropagation artificial neural network, artificial neural network trained with black hole optimization, extreme learning machine, random forest and, adaboost. It performs better than these systems. Experiments have also been performed to analyze the performance of the proposed system under different parameter values viz. number of ELMs in V-ELM and number of hidden layer neurons in a single ELM.