Chunhui Li1,2, Xiaoying Wang1,2,*, Qingjie Zhang1,2, Jiaye Liang1, Aijing Zhang1
CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 645-674, 2025, DOI:10.32604/cmc.2024.058081
- 03 January 2025
Abstract Previous studies have shown that deep learning is very effective in detecting known attacks. However, when facing unknown attacks, models such as Deep Neural Networks (DNN) combined with Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) combined with LSTM, and so on are built by simple stacking, which has the problems of feature loss, low efficiency, and low accuracy. Therefore, this paper proposes an autonomous detection model for Distributed Denial of Service attacks, Multi-Scale Convolutional Neural Network-Bidirectional Gated Recurrent Units-Single Headed Attention (MSCNN-BiGRU-SHA), which is based on a Multi-strategy Integrated Zebra Optimization Algorithm (MI-ZOA). The… More >
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