We proposed Deep Convolutional Neural Network Bi Long-Short Term Memory (DCNNBiLSTM) and Deep Convolutional Neural Network Gated Recurring Unit (DCNNGRU) to detect DDoS attacks.
In this study, we provide Deep Neural Network (DNN) based approaches to detecting Distributed Denial-of-Service (DDoS) attacks. In order to improve the DNN’s accuracy, the suggested approaches use two different hybrid DNN scenario detections to demonstrate the possibilities. As training and testing data, we use the publicly available Intrusion Detection datasets; CIC-IDS2017 and CIC-DDoS2019. Experiments have shown that the presented approaches are 99.9% effective at detecting attacks.
2023 IEEE 8th International Conference for Convergence in Technology (I2CT)
10.1109/I2CT57861.2023.10126434
https://ieeexplore.ieee.org/document/10126434
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