Review on the application of deep learning in network attack detection

With the development of new technologies such as big data, cloud computing, and the Internet of Things, network attack technology is constantly evolving and upgrading, and network attack detection technology is forced to undergo corresponding iterative evolution. Three main problems are associated with these technologies: the automatic representation of heterogeneous and complex network traffic data, the uneven network attack samples, and the contradiction between the accuracy of the anomaly detection model and the continuous evolution of attacks. Researchers have proposed several network attack detection techniques based on deep learning to address these problems. This study reviews and analyzes the studies aimed at dealing with such problems, considering multiple factors, such as models, traffic representation and feature extraction, threat detection model training, and model robustness improvement. Finally, the existing problems and challenges associated with the current research are analyzed with respect to data category imbalance, high-dimensional massive data processing, concept distribution drift, real-time interpretability of the detection model, and the security of the model.

Automated summary

With the development of new technologies such as big data, cloud computing, and the Internet of Things, the network attack technology is evolving, and network attack detection technology needs to evolve accordingly. Several network attack detection techniques based on deep learning have been proposed to address the problems of heterogeneous traffic data, uneven attack samples, and the contradiction between accuracy and attack evolution. This study reviews and analyzes these techniques, considering factors such as models, traffic representation, training, and model robustness. The existing problems and challenges related to data imbalance, high-dimensional data processing, concept distribution drift, real-time interpretability, and model security are also discussed.