Network Security Intrusion Detection System Based on Deep Learning

In order to build a new, efficient, and accurate network security intrusion detection system, this paper is based on machine algorithms of deep learning. Firstly, a corresponding network data processing layer is set up, which involves data collection and data preprocessing, and can effectively process and convert various raw network data. Then, a deep learning detection layer is set up, which can timely and accurately identify various network intrusion detection threats. Finally, a decision and feedback layer is set up, which can timely warn of identified network intrusion detection threats and take effective defense measures to ensure network security. After experimental verification, the results show that deep learning based network security intrusion detection systems have excellent stability and accuracy in both intrusion behavior classification and recognition when facing network intrusions. In terms of detection on different datasets, network security intrusion detection systems based on deep learning are significantly superior to other systems. This result confirms that the system’s generalization ability is outstanding when testing different datasets.