[1] Cisco. Cisco visual networking index:forecast and trends,2017-2022 white paper[EB/OL].[2020-04-25]. https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-indexvni/white-paper-c11-741490.html. [2] ANDERSON J P. Computer security threat monitoring and surveillance[EB/OL].[2020-04-25]. http://seclab.cs.ucdavis.edu/projects/history/papers/ande80.pdf. [3] SHON T,MOON J. A hybrid machine learning approach to network anomaly detection[J]. Information Sciences,2007,177(18):3799-3821. [4] 赵夫群. 基于混合核函数的LSSVM网络入侵检测方法[J]. 现代电子技术,2015,38(21):96-99.(ZHAO F Q. Detection method of LSSVM network intrusion based on hybrid kernel function[J]. Modern Electronics Technique,2015,38(21):96-99.) [5] JHA S,TAN K M C,MAXION R A. Markov chains,classifiers, and intrusion detection[C]//Proceedings of the 14th IEEE Computer Security Foundations Workshop. Piscataway:IEEE, 2001:206-219. [6] BAMAKAN S M H,WANG H,SHI Y. Ramp loss k-support vector classification-regression:a robust and sparse multi-class approach to the intrusion detection problem[J]. Knowledge-Based Systems, 2017,126:113-126. [7] PAN X,LUO Y,XU Y. K-nearest neighbor based structural twin support vector machine[J]. Knowledge-Based Systems,2015,88:34-44. [8] SALAMA M A, EID H F, RAMADAN R A, et al. Hybrid intelligent intrusion detection scheme[M]//GASPAR-CUNHA A, TAKAHASHI R, SCHAEFER G, et al. Soft Computing in Industrial Applications, AINSC 96. Berlin:Springer, 2011:293-303. [9] ALOM M Z,BONTUPALLI V,TAHA T M. Intrusion detection using deep belief networks[C]//Proceedings of the 2015 Nation Aerospace and Electronics Conference. Piscataway:IEEE,2015:339-344. [10] JAVAID A,NIYAZ Q,SUN W,et al. A deep learning approach for network intrusion detection system[C]//Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies. Brussels:Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering,2015:21-26. [11] KHAN R U,ZHANG X,KUMAR R. Analysis of ResNet and GoogleNet models for malware detection[J]. Journal of Computer Virology and Hacking Techniques,2018,15(1):29-37 [12] KHAN R U,ZHANG X,KUMAR R,et al. Evaluating the performance of ResNet model based on image recognition[C]//Proceedings of the 2018 International Conference on Computing and Artificial Intelligence. New York:ACM,2018:86-90. [13] KUMAR R, ZHANG X KHAN R U, et al. Malicious code detection based on image processing using deep learning[C]//Proceedings of the 2018 International Conference on Computing and Artificial Intelligence. New York:ACM,2018:81-85. [14] 贾凡, 孔令智. 基于卷积神经网络的入侵检测算法[J]. 北京理工大学学报,2017,37(12):1271-1275.(JIA F,KONG L Z. Intrusion detection algorithm based on convolutional neural network[J]. Transactions of Beijing Institute of Technology,2017,37(12):1271-1275.) [15] BONTEMPS L,CAO VL,MCDERMOTT J,et al. Collective anomaly detection based on long short-term memory recurrent neural network[C]//Proceedings of the 3rd Internet Conference on Future Data and Security Engineering, LNCS 10018. Cham:Springer,2016:141-152. [16] 王伟. 基于深度学习的网络流量分类及异常检测方法研究[D]. 合肥:中国科学技术大学,2018:61-75.(WANG W. Deep learning for network traffic classification and anomaly detection[D]. Hefei:University of Science and Technology of China,2018:61-75.) [17] 何文河, 李陶深, 黄汝维. 云环境下基于改进BP算法的入侵检测模型[J]. 计算机技术与发展,2016,26(2):87-90.(HE W H,LI T S,HUANG R W. Intrusion detection model based on improved BP algorithm in cloud environment[J]. Computer Technology and Development,2016,26(2):87-90.) [18] LIN M,CHEN Q,YAN S,Network in network[EB/OL].[2020-12-11]. https://arxiv.org/pdf/1312.4400v3.pdf. |