Real-time Application of Intrusion Detection Algorithm Based on Machine Learning in Security System

The purpose of this article is to design and implement an intrusion detection system (IDS) based on machine learning to improve the network security protection ability. In order to achieve this goal, the advanced model in deep learning is adopted, and an efficient intrusion detection model is constructed through training data sets, and the model parameters are carefully optimized and adjusted by using the verification set. After the model is successfully built, the system performance is comprehensively evaluated by using an independent test set. The results show that compared with the traditional IDS, the deep learning model proposed in this article has obvious advantages in key performance indicators such as accuracy, recall and $F 1$ value. In addition, the detection time of the system is much better than that of the traditional methods, and it can quickly respond to potential network threats. IDS based on deep learning has a significant effect in improving network security protection. It can not only detect intrusion more accurately and comprehensively, but also greatly shorten the detection time, providing a strong guarantee for network security.