Application research of a data stream clustering algorithm in network security defense

0Citations
Citations of this article
8Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

The traditional intrusion detection system feature model is based on static data mining. Its mining algorithm relies on too many assumptions, which makes it difficult for intrusion detection systems to adapt to dynamic and real-time system detection requirements. Using attenuated sliding window technology, data stream mining technology and fusion technology with intrusion detection system, a data flow clustering algorithm based on attenuated sliding window is designed to improve and optimize the feature pattern extraction method of intrusion detection system to solve the dynamics of intrusion detection system. Through algorithm design, algorithm application and intrusion detection system simulation verification, the feasibility and accuracy of the algorithm and the optimized intrusion detection system are proved.

Cite

CITATION STYLE

APA

Zhu, C., Wang, X., & Zhu, L. (2019). Application research of a data stream clustering algorithm in network security defense. In Journal of Physics: Conference Series (Vol. 1423). Institute of Physics Publishing. https://doi.org/10.1088/1742-6596/1423/1/012027

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free