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

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Bibliographic Details
Title:	Optimization of Incremental Network Intrusion Real-Time Detection Algorithm Based on Computer Data Simulation.
Authors:	Xiong, Hui¹ xionghui811129@163.com, Yuan, Chao¹ yuanchao_scvtc1983@163.com, Zhao, Ping¹ zhaoping667788@163.com
Source:	Journal of Computing & Information Technology. Jun2026, Vol. 34 Issue 2, p81-98. 18p.
Subjects:	Intrusion detection systems (Computer security), Computer network security, Synthetic data, Machine learning, Cyberterrorism, Optimization algorithms, Real-time computing
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. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
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. [ABSTRACT FROM AUTHOR]
ISSN:	13301136
DOI:	10.20532/cit.2026.1006347