Information Security Network Intrusion Detection System Based on Machine Learning
Feng Guo, Hanlin Jiao, Xiong Zhang, Yuting Zhou, Hao Feng · 2024
Nowadays, more and more people are becoming aware of the security issues of computers. Traditional IDS (Intrusion Detection System) has the drawbacks of poor real-time performance and low accuracy. In response to this issue, this article adopts a machine learning(ML) based network intrusion detection algorithm. The intrusion detection module can use SVM (Support Vector Machine), deep learning, etc., to construct models. The real-time detection and response module can use the established model to monitor and analyze network traffic in real-time, in order to achieve rapid detection and response to network attacks. In the real-time performance test results, the accuracy from 08:00 to 09:00 is 0.92, the false alarm rate is 0.05, and the average detection time is 50 milliseconds; The accuracy from 09:00 to 10:00 is 0.90, the false alarm rate is 0.06, and the average detection time is 55 milliseconds. This article studies a machine learning based method for detecting and identifying network attack behaviors, which is beneficial for improving the level of network security defense.