[1] 黄谦,王震,韦韬,等.基于One-Class SVM的实时入侵检测系统[J].计算机工程,2006,32(16):133-135.(HUANG Q, WANG Z, WEI T, et al. A Real-time intrusion detection system based on One-Class SVM[J]. Computer Engineering, 2006, 32(16):133-135.)
[2] 戚名钰,刘铭,傅彦铭.基于PCA的SVM网络入侵检测研究[J].信息网络安全,2015(2):15-18.(QI M Y, LIU M, FU Y M. Research on network intrusion detection using support vector machines based on principal component analysis[J]. Netinfo Security, 2015(2):15-18.)
[3] ZHOU C, PAFFENROTH R C. Anomaly detection with robust deep autoencoders[C]//Proceedings of the 2017 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM, 2017:665-674.
[4] 袁静,章毓晋.融合梯度差信息的稀疏去噪自编码网络在异常行为检测中的应用[J].自动化学报,2017,43(4):604-610.(YUAN J, ZHANG Y J. Application of sparse denoising auto encoder network with gradient difference information for abnormal action detection[J]. Acta Automatica Sinica, 2017, 43(4):604-610.)
[5] 陈虹,万广雪,肖振久.基于优化数据处理的深度信念网络模型的入侵检测方法[J].计算机应用,2017,37(6):1636-1643.(CHEN H, WAN G X, XIAO Z J. Intrusion detection method of deep belief network model based on optimization of data processing[J]. Journal of Computer Applications, 2017, 37(6):1636-1643.)
[6] QU F, ZHANG J, SHAO Z, et al. An intrusion detection model based on deep belief network[C]//Proceedings of the 2017 VI International Conference on Network, Communication and Computing. Kunming:[s.n.], 2017:97-101.
[7] 魏明军,王月月,金建国.一种改进免疫算法的入侵检测设计[J].西安电子科技大学学报,2016,43(2):126-131.(WEI M J, WANG Y Y, JIN J G. Intrusion detection design of the impoved immune algorithm[J]. Journal of Xidian University, 2016, 43(2):126-131.)
[8] 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.)
[9] 王明,李剑.基于卷积神经网络的网络入侵检测系统[J].信息安全研究,2017,3(11):990-994.(WANG M, LI J. Network intrusion detection model based on convolutional neural network[J]. Journal of Information Security Research, 2017, 3(11):990-994.)
[10] DUMOULIN V, BELGHAZI I, POOLE B, et al. Adversarially learned inference[J]. ArXiv Preprint, 2016, 2016:1606.00704.
[11] VINCENT P, LAROCHELLE H, BENGIO Y, et al. Extracting and composing robust features with denoising autoencoders[C]//Proceedings of the 25th International Conference on Machine Learning. New York:ACM, 2008:1096-1103.
[12] ZHAI S, CHENG Y, LU W, et al. Deep structured energy based models for anomaly detection[J]. ArXiv Preprint, 2016, 2016:1605.00717.
[13] ZONG B, SONG Q, MIN M R, et al. Deep autoencoding Gaussian mixture model for unsupervised anomaly detection[C]//Proceedings of the 2018 International Conference on Learning Representations. Vancouver:ICLR, 2018:1203-1224.
[14] SCHLEGL T, SEEBÖCK P, WALDSTEIN S M, et al. Unsupervised anomaly detection with generative adversarial networks to guide marker discovery[C]//Proceedings of the 2017 International Conference on Information Processing in Medical Imaging. Berlin:Springer, 2017:146-157.
[15] METZ L, POOLE B, PFAU D, et al. Unrolled generative adversarial networks[J]. ArXiv Preprint, 2016, 2016:1611.02163. |