Abstract: This paper introduces deepnetguard, an innovative deep learning algorithm designed to efficiently identify potential security threats in large-scale network traffic.deepnetguard achieves automated feature learning by fusing basic, statistical, and behavioral features through a multi-level feature extraction strategy, and is capable of identifying both short-time patterns and long-time dependencies. To adapt to the dynamic network environment, the algorithm introduces a dynamic weight adjustment mechanism that allows the model to self-optimize the importance of features based on real-time traffic changes. In addition, deepnetguard integrates auto-encoder (ae) and generative adversarial network (gan) technologies to not only detect known threats, but also recognize unknown threats. By applying the attention mechanism, deepnetguard enhances the interpretability of the model, enabling security experts to track and understand the key factors in the model's decision-making process. Experimental evaluations show that deepnetguard performs well on multiple public datasets, with significant advantages in accuracy, recall, precision, and f1 scores over traditional ids systems and other deep learning models, demonstrating its strong performance in cyber threat detection.

Jinjin Chao and Tian Xie, “Deep Learning-Based Network Security Threat Detection and Defense” International Journal of Advanced Computer Science and Applications(IJACSA), 15(11), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151164

@article{Chao2024,
title = {Deep Learning-Based Network Security Threat Detection and Defense},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151164},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151164},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {11},
author = {Jinjin Chao and Tian Xie}
}