Abstract
A Distributed Denial of Service (DDoS) attack occurs when an intruder or a group of attackers attempts to block legitimate users from accessing a service. A DoS attack is carried out by a single system, while a DDoS attack is carried out by numerous systems. DDoS attacks can be directed at several OSI layers. Deep learning has played a crucial role in the advancement of intrusion detection technologies in recent years. The main purpose of this work is to detect and identify DDoS attacks in the OSI model’s application, network, and transport layers using deep learning models. The proposed models have been evaluated against the CICDDoS2019 dataset which consists of application, network and transport layer DDoS attacks. For the CICIDDOS2019 dataset, Long-Short-Term memory and Gated Recurrent Unit attained an average accuracy of 99.4% and 92.5%, respectively. We also compared the suggested models’ performance to that of a few other higher accuracy models and found that the proposed models have higher accuracy with fewer epochs. In addition, the performance of the proposed system is also evaluated for various types of DDoS attacks in the CICDDoD2019 dataset and LSTM is found to produce good accuracy.
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Subrmanian, M., Shanmugavadivel, K., Nandhini, P.S., Sowmya, R. (2022). Evaluating the Performance of LSTM and GRU in Detection of Distributed Denial of Service Attacks Using CICDDoS2019 Dataset. In: Kim, J.H., Deep, K., Geem, Z.W., Sadollah, A., Yadav, A. (eds) Proceedings of 7th International Conference on Harmony Search, Soft Computing and Applications. Lecture Notes on Data Engineering and Communications Technologies, vol 140. Springer, Singapore. https://doi.org/10.1007/978-981-19-2948-9_38
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