Malicious Intrusion Data Mining Algorithm of Wireless Personal Communication Network Supported by Legal Big Data

Wireless personal communication network is easily affected by intrusion data in the communication process, resulting in the inability to ensure the security of personal information in wireless communication. Therefore, this paper proposes a malicious intrusion data mining algorithm based on legitimate big data in wireless personal communication networks. The clustering algorithm is used to iteratively obtain the central point of malicious intrusion data and determine its expected membership. The noise in malicious intrusion data is denoised by objective function, and the membership degree of communication data is calculated. The change factor of the neighborhood center of gravity of malicious intrusion data in wireless personal communication network is determined, the similarity between the characteristics of malicious intrusion data by using the Markov distance was determined, and the malicious intrusion data mining of wireless personal communication network supported by legal big data was completed. The experimental results show that the accuracy of mining malicious data is high and the mining time is short.