Design and Implementation of Artificial Intelligence-Driven Network Intrusion Detection System

The current network intrusion detection systems (IDS) have poor adaptive learning capabilities under large-scale network traffic, resulting in low detection quality and efficiency. This article investigates the design and implementation of an artificial intelligence (AI)-driven network IDS to improve detection accuracy and real-time performance. Firstly, the one-hot encoding conversion method is used to denoise large-scale data. Then, a One-Class support vector machine (SVM) is used to optimize the adaptive learning and training process. Finally, real-time detection and updates are achieved by sliding the window to process data within a fixed time. The experimental results show that compared to the baseline systems of convolutional recurrent neural networks (RNN) and new deep neural networks (NDNN), the average accuracy of the system in this article is 4.7% and 6.5% higher, respectively, and the average response time decreases by 2.5 seconds and 4.1 seconds, respectively. The results indicate that AI-driven network IDS can provide more effective solutions for network security.