Strategic Application of AI Intelligent Algorithm in Network Threat Detection and Defense

Authors

  • Kuo Wei Computer Science,Individual Contributor,Shenzhen, China
  • Hengyi Zang Big Data and Business Intelligence,Independent research,Shanghai,China
  • Yiming Pan Computer science,colorado technical university,Austin, TX,USA
  • Guanghui Wang Computer Science,Independent contributor,Shanghai
  • Zepeng Shen Network Engineering, Shaanxi University of Technology, Shaanxi 723001, China

DOI:

https://doi.org/10.53469/jtpes.2024.04(01).07

Keywords:

Network security, Intrusion detection, Security defense, k-means clustering

Abstract

With the rapid development of information technology, the network has become the main platform for important activities such as People's Daily life, business and government. However, the issue of network security has increasingly become the focus of attention. The rise of cyber intrusions has threatened the security of individuals, businesses and governments. Therefore, intrusion detection technology has become one of the important components of network security. In this paper, the algorithm technology combined with artificial intelligence is used to realize the application status of network security system intrusion detection and defense. The traditional K-means clustering algorithm has problems such as low efficiency, poor detection accuracy and passive processing in network intrusion behavior detection, such as preprocessing and k value determination. In order to solve the above problems, an improved k-means clustering algorithm network security detection model is adopted, and the detection model experiment is realized with the help of data sets.

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Published

2024-01-25

How to Cite

Wei, K., Zang, H., Pan, Y., Wang, G., & Shen, Z. (2024). Strategic Application of AI Intelligent Algorithm in Network Threat Detection and Defense. Journal of Theory and Practice of Engineering Science, 4(01), 49–57. https://doi.org/10.53469/jtpes.2024.04(01).07

Issue

Vol. 4 No. 01 (2024): JTPES-2023-4-1

Section

Articles

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Copyright (c) 2024 Kuo Wei, Hengyi Zang, Yiming Pan, Guanghui Wang, Zepeng Shen

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.