Abstract
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.
Highlights
With the rapid development of electronic information technology, wireless communication technology has become the most critical technology in current communication
With the support of legal big data, researchers in this field have done a lot of mining for malicious intrusion data in wireless personal communication network to ensure the security of personal communication information
In order to solve the shortcomings of the above methods, this paper proposes a malicious intrusion data mining algorithm for wireless personal communication networks supported by legal big data
Summary
With the rapid development of electronic information technology, wireless communication technology has become the most critical technology in current communication. Suppose ρi describes the length between the center of gravity aðρÞ and a + 1ðρÞ of malicious intrusion data in a wireless personal communication network; there is ρi = D1⁄2aðρÞ + a − 1ðρÞ: ð9Þ At this time, the malicious data center of gravity in wireless personal communication is the largest, but because of the fluctuation of its peripheral data, the data center of gravity cannot always be kept as the maximum value [14], and the absolute difference between aðρÞ and a + 1ðρÞ needs to be calculated to obtain. In the formula, σ and θ represent the nonlinear feature of malicious data in different dimensions, respectively On this basis, the construction of malicious intrusion data mining model of wireless personal communication network is completed, that is.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
