A Hybrid Deep Neural Network Approach for Enhanced Network Intrusion Detection

With the rapid development of Internet technology, network security issues have become increasingly severe, making intrusion detection systems more important. Traditional intrusion detection methods face limitations when dealing with complex attack patterns and large-scale data. This study proposes a network intrusion detection system based on a hybrid deep neural network, integrating the strengths of Convolutional Neural Networks, Long Short-Term Memory networks, and Transformer models to enhance intrusion detection performance. Experimental results demonstrate that the proposed method outperforms traditional algorithms, effectively identifying various types of network attacks with strong scalability and robustness, especially when handling large-scale data.