
Application research of a data stream clustering algorithm in network security defense
Author(s) -
Chenyang Zhu,
Xiaoyang Wang,
Lin Zhu
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1423/1/012027
Subject(s) - intrusion detection system , cluster analysis , sliding window protocol , computer science , data mining , anomaly based intrusion detection system , network security , feature (linguistics) , window (computing) , data stream , algorithm , artificial intelligence , computer security , linguistics , philosophy , operating system , telecommunications
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.