Optimization of Incremental Network Intrusion Real-Time Detection Algorithm Based on Computer Data Simulation

Abstract

High-quality and representative network intrusion data is the basis for building a network intrusion detection system. Given the current situation in which the use of traditional algorithms is difficult to satisfy the demands of real-time detection, this paper constructs a network intrusion data simulation method based on LDMs, and combines it with the improved LST model to perform real-time detection of incremental network intrusions. The experiments demonstrated that the LST model outperformed the comparison models in key indicators such as detection rate, precision rate, recall rate, and F1-score, and showed significant advantages in running time. After the specific LST model converged, its accuracy, recall, and F1-score were 0.983, 0.971, and 0.958, respectively. When dealing with advanced persistent threat attacks and zero-day attacks, the detection rate reached 97.4% and 96.8%, and the running time was 160.3 s and 155.2 s, respectively. The LST model not only has high accuracy in detecting complex network intrusion behaviors, but also has obvious advantages in operating efficiency. This paper provides a strong guarantee for network security, helps to promptly discover and prevent potential network threats, and protects the stable operation of network systems.