DIDDOS: An approach for detection and identification of Distributed Denial of Service (DDoS) cyberattacks using Gated Recurrent Units (GRU)

Distributed Denial of Service (DDoS) attacks can put the communication networks in instability by throwing malicious traffic and requests in bulk over the network. Computer networks form a complex chain of nodes resulting in a formation of vigorous structure. Thus, in this scenario, it becomes a challenging task to provide an efficient and secure environment for the user. Numerous approaches have been adopted in the past to detect and prevent DDoS attacks but lack in providing efficient and reliable attack detection. As a result, there is still notable room for improvement in providing security against DDoS attacks. In this paper, a novel high-efficient approach is proposed named DIDDOS to protect against real-world new type DDoS attacks using Gated Recurrent Unit (GRU) a type of Recurrent Neural Network (RNN). Different classification algorithms such as Gated Recurrent Units (GRU), Recurrent Neural Networks (RNN), Naive Bayes (NB), and Sequential Minimal Optimization (SMO) are utilized to detect and identify DDoS attacks. Performance evaluation metrics like accuracy, recall, f1-score, and precision are used to evaluate the efficiency of the machine and deep learning classifiers. Experimental results yield the highest accuracy of 99.69% for DDoS classification in case of reflection attacks and 99.94% for DDoS classification in case of exploitation attacks using GRU. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

DDoS attacks can disrupt communication networks, and the DIDDOS approach is effective in preventing new types of DDoS attacks, achieving high accuracy of 99.69% and 99.94% using GRU neural network for classification.