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.

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.

[1] Youcef AMIRAT,Arnaud MÜ,NCH. On the Controllability of an Advection-diffusion Equation with Respect to the Diffusion Parameter: Asymptotic Analysis and Numerical Simulations[J]. Acta Mathematicae Applicatae Sinica, English Serie, 2019, 35(1): 54-110.
[2] Wei-ke WANG,Yu-tong WANG. The Well-Posedness of Solution to Semilinear Pseudo-parabolic Equation[J]. Acta Mathematicae Applicatae Sinica, English Serie, 2019, 35(2): 386-400.
[3] Al-Hussein A, Haldar A. Unscented Kalman filter with unknown input and weighted global iteration for health assessment of large structural systems. Structural Control and Health Monitoring, 2015, 23(1),156-175
[4] Liu Yilong, Liu Jie, Liu Jiangnan. Research on composite inversion of dynamic loads and structural parameters based on sub-structure analysis. Journal of Mechanical Strength, 2013, 35(5),553-558.
[5] Noël J P, Kerschen G. Nonlinear system identification in structural dynamics,10 more years of progress. Mechanical Systems and Signal Processing, 2017, 83,2-35.
[6] ALI M S, TABASSUM H, HOSSAIN E. Dynamic user clustering and power allocation for uplink and downlink Non-Orthogonal Multiple Access (NOMA) systems[J]. IEEE Access, 2016, 4:6325-6343.
[7] KIM B, CHUNG W, LIM S, et al. Uplink NOMA with multi-antenna[C]//Proceedings of the 2015 IEEE 81st Vehicular Technology Conference. Piscataway, NJ:IEEE, 2015:1-5.
[8] JIANG Y Z, CHUNG F L, WANG S T, et al. Collaborative fuzzy clustering from multiple weighted views[J]. IEEE Transactions on Cybernetics, 2015, 45(4): 688-701.
[9] TU Binbin, CHUAI Rongyan, XU Hui. Outlier Detection Based on K-mean Distance Outlier Factor for Gait Signal[J]. Information and control, 2019, 48(1): 16-21.
[10] ZHOU S B, XU W X. A novel clustering algorithm based on relative density and decision graph[J]. Control and Decision, 2018, 33(11):1921-1930.
[11] HE H, TAN Y. Automatic pattern recognition of ECG signals using entropy-based adaptive dimensionality reduction and clustering[J]. Applied Soft Computing,2017, 55:238-252
[12] ZHU Yuelong, ZHU Xiaoxiao, WANG Jimin. Time series motif discovery algorithm based on subsequence full join and maximum clique. Journal of Computer Applications, 2019, 39(2): 414-420.
[13] HUANG S C, LIU Y. Classification algorithm for noisy and dynamic data stream[J]. Journal of Jiangsu University of Science and Technology (Natural Science Edition), 2016, 30(3):281-285.
[14] SUN B, WANG J D, CHEN H Y, et al. Diversity measures inensemble learning[J]. Control and Decision, 2014, 29(3):385-395.
[15] ZHOU Yuhao, ZHANG Hongling, LI Fangfei, QI Peng. Local focus support vector machine algorithm[J]. Journal of Computer Applications, 2018, 38(4): 945-948.