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

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.