Academic Journal of Computing & Information Science, 2019, 2(3); doi: 10.25236/AJCIS.020304.
LIN Yuanjian*, YANG Fei
Nanchang Institute of Science and Technology, Nanchang 330108, Jiangxi
*corresponding author e-mail: firstname.lastname@example.org
The routing conflict of deep wireless communication network is easy to lead to channel imbalance. In order to improve the security trend of deep wireless communication network, a security trend prediction algorithm of deep wireless communication network based on Kalman fusion filter detection is proposed. The link communication signal reflecting the security trend of deep wireless communication network is extracted, and the channel model of deep wireless communication network is reorganized by using grid packet switching structure. The characteristic quantity of security trend attribute of communication signal in deep wireless communication network is extracted, and the security trend data of deep wireless communication network is detected by autocorrelation matching filter detection method, and the transmission channel equilibrium processing of deep wireless communication network is realized. Combined with association rule mining and matching filtering technology, the security trend of deep wireless communication network is predicted accurately. The simulation results show that the accuracy of this method for predicting the security trend of deep wireless communication network is high, and the balance of transmission channel in deep wireless communication network is improved.
big data; network security; trend prediction; wireless communication; channel
LIN Yuanjian, YANG Fei. Research on Prediction Method of Network Security Trend Based on Big Data. Academic Journal of Computing & Information Science (2019), Vol. 2, Issue 3: 22-33. https://doi.org/10.25236/AJCIS.020304.
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