Dynamic detection of malicious intrusion in wireless network based on improved random forest algorithm

In order to improve the malicious nonlinear scrambling intrusion detection ability of wireless network and ensure the security of the network, a malicious nonlinear scrambling intrusion detection method based on improved random forest algorithm is proposed. The wireless network malicious nonlinear scrambling intrusion signal model is constructed, the physical layer characteristic detection method of terminal equipment is used to decompose the characteristics of the wireless network malicious nonlinear scrambling intrusion signal, the spectrum characteristic quantity of the wireless network malicious nonlinear scrambling intrusion is extracted by the analysis method of test statistics and detection threshold, and the signal characteristic reorganization and fuzzy clustering analysis are realized by using the improved random forest algorithm for the extracted spectrum characteristics of the wireless network malicious nonlinear scrambling intrusion. According to the clustering distribution of spectral features of malicious nonlinear scrambling intrusion signals in wireless networks, the optimal detection of malicious nonlinear scrambling intrusion features in wireless networks is realized under random forest learning by adopting the methods of reinforcement learning decision and static feature fusion. The simulation results show that the feature clustering of malicious non-linear scrambling intrusion detection in wireless networks is good, and the probability of accurate identification of intrusion information is high.