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What is GAN-CNN-BiLSTM for Intrusion Detection about?

This paper presents a GAN-CNN-BiLSTM model for network intrusion detection, addressing the challenges of data imbalance in network traffic datasets. The proposed model combines Generative Adversarial Networks (GAN) for data expansion with Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to enhance detection accuracy and efficiency. Experimental results demonstrate that this model outperforms traditional machine learning methods, providing a robust solution for various se

Author

Arundhathi

Language

EN