[1] 霍大军.网络化集群作战研究[M].北京:国防大学出版社,2013:66-68.HUO D J.Operation of network swarm[M].Beijing:National Defense University Press,2013:66-68(in Chinese). [2] 梁晓龙,何吕龙,张佳强,等.航空集群构型控制及其演化方法[J].中国科学:技术科学,2019,49(3):277-287.LIANG X L,HE L L,ZHANG J Q,et al.Configuration control and evolutionary mechanism of aircraft swarm[M].Scientia Sinica(Technologica),2019,49(3):277-287(in Chinese). [3] CAO X B,YANG P,MOHAMED A,et al.Airborne communication networks:A survey[J].IEEE Journal on Selected Areas in Communications,2018,36(9):1907-1926. [4] 吕娜,杜思深,张岳彤,等.数据链理论与系统[M].北京:电子工业出版社,2018:7-9.LV N,DU S S,ZHANG Y T,et al.Theory and system of data link[M].Beijing:Publishing House of Electronics Industry,2018:7-9(in Chinese). [5] 梁一鑫,程光,郭晓军,等.机载网络体系结构及其协议栈研究进展[J].软件学报,2016,27(1):96-111.LIANG Y X,CHENG G,GUO X J,et al.Research progress on architecture and protocol stack of the airborne network[J].Journal of Software,2016,27(1):96-111(in Chinese). [6] NGUYEN T T,ARMITAGE G.A survey of techniques for internet traffic classification using machine learning[J].IEEE Communications Surveys & Tutorials,2009,10(4):56-76. [7] SHAFIQ M,YU X,LAGHARI A A,et al.Network traffic classification techniques and comparative analysis using machine learning algorithms[C]//20162nd IEEE International Conference on Computer and Communications (ICCC).Piscataway:IEEE Press,2016:2451-2455. [8] 李勤,师维,孙界平,等.基于卷积神经网络的网络流量识别技术研究[J].四川大学学报(自然科学版),2017,54(5):959-964.LI Q,SHI W,SUN J P,et al.The research of network traffic identification based on convolutional neural network[J].Journal of Sichuan University (Natural Science Edition),2017,54(5):959-964(in Chinese). [9] 王勇,周慧怡,俸皓,等.基于深度卷积神经网络的网络流量分类方法[J].通信学报,2018,39(1):14-23.WANG Y,ZHOU H Y,FENG H,et al.Network traffic classification method basing on CNN[J].Journal on Communications,2018,39(1):14-23(in Chinese). [10] SHI H,LI H,DAN Z,et al.An efficient feature generation approach based on deep learning and feature selection techniques for traffic classification[J].Computer Networks,2018,132:81-98. [11] WEI W,SHENG Y,WANG J,et al.HAST-IDS:Learning hierarchical spatial-temporal features using deep neural networks to improve intrusion detection[J].IEEE Access,2018,6(99):1792-1806. [12] ZOU Z,GE J,ZHENG H, et al.Encrypted traffic classification with a convolutional Long short-term memory neural network[C]//2018 IEEE 20th International Conference on High Performance Computing and Communications.Piscataway:IEEE Press,2018:329-334. [13] TONG W,ZHEN H,WEI W,et al.Early-stage internet traffic identification based on packet payload size[J].Journal of Southeast University(English Edition),2014,30(3):289-295. [14] 范竣翔,李琦,朱亚杰,等.基于RNN的空气污染时空预报模型研究[J].测绘科学,2017,42(7):76-83.FAN J X,LI Q,ZHU Y J,et al.A spatio-temporal prediction framework for air pollution based on deep RNN[J].Science of Surveying and Mapping,2017,42(7):76-83(in Chinese). [15] GREFF K,SRIVASTAVA R K,KOUTNÍK J,et al.LSTM:A search space odyssey[J].IEEE Transactions on Neural Networks & Learning Systems,2016,28(10):2222-2232. [16] DONAHUE J,HENDRICKS L A,ROHRBACH M,et al.Long-term recurrent convolutional networks for visual recognition and description[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2014,39(4):677-691. [17] LIU J,SHAHROUDY A,XU D,et al.Spatio-temporal LSTM with trust gates for 3D human action recognition[C]//European Conference on Computer Visio.Berlin:Springer,2016:816-833. |