Deep Learning-Based Network Intrusion Detection Systems: A Comprehensive Survey of Methods, Evolution, and Research Perspectives

Network Intrusion Detection Systems (NIDS), indispensable as they are, are facing extremely sophisticated, evasive, and large-scale cyberattacks in modern networks. The traditional signature-based approaches still work for known threats, but fail to detect zero-day attacks and also seem to struggle with detecting dynamically evolving traffic patterns. That is the reason deep learning (DL) has emerged as a game-changer for NIDS, particularly because of its capability to automatically extract complex representations from high-volume network data. In the past 10 years, there have been a multitude of different deep learning architectures proposed, including autoencoders, convolutional neural networks (CNN), recurrent neural networks (RNN), long shortterm memory (LSTM) networks, hybrid CNN-LSTM models, and graph neural networks (GNN) along with recently proposed transformer-based attention mechanisms to enhance accuracy and robustness against intrusion detection systems. This survey aims to provide a detailed review of DL-based NIDS with a focus on the chronological evolution of techniques, methodological innovations, and the limitations therein. Then highly influential research works are discussed in detail, focusing on how later work addressed the problems of those earlier approaches. Following this will be a taxonomy of DL-based NIDS, followed by comparative insights, open challenges, and perspectives for future research including interpretable DL, adversarial resilience, federated learning, and behavior-driven hybrid models.