[1] |
国家互联网应急中心. 2017年7月我国互联网安全威胁报告[R]. 2017.
|
|
National Internet Emergency Center. The report of China’s Internet security threat in July[R]. 2017.
|
[2] |
LEI Y , LIU J , YIN H . Intrusion detection techniques based on improved intuitionist fuzzy neural networks[J]. Applied Mechanics& Materials, 2014,713-715(1): 2507-2510.
|
[3] |
THASEEN I S , KUMAR C A . Intrusion detection model using fusion of chi-square feature selection and multi class SVM[J]. Journal of King Saud University-Computer and Information Sciences, 2016,29(4): 462-472.
|
[4] |
DASTANPOUR A , IBRAHIM S , MASHINCHI R . Comparison of genetic algorithm optimization on artificial neural network and support vector machine in intrusion detection system[C]// IEEE International Conference on Open Systems, 2014: 72-77.
|
[5] |
ABDLHAMED M , KIFAYAT K , SHI Q . Intrusion prediction systems[J]. Information Fusion for Cyber-Security Analytics, 2017,69(1): 155-174.
|
[6] |
PARSAEI M , ROSTAMI S , JAVIDAN R . A hybrid data mining approach for intrusion detection on imbalanced NSL-KDD dataset[J]. International Journal of Advanced Computer Science and Applications, 2016,7(6): 20-25.
|
[7] |
POZI M , SULAIMAN M , MUSTAPHA N . Improving anomalous rare sttack detection rate for intrusion detection system using support vector machine and genetic programming[J]. Neural Processing Letters, 2016,44(2): 279-290.
|
[8] |
高妮, 高岭, 贺毅岳 . 基于自编码网络特征降维的轻量级入侵检测模型[J]. 电子学报, 2017,45(3): 730-739.
|
|
GAO N , GAO L , HE Y Y . A lightweight intrusion detection model based on autoencoder network with feature reduction[J]. Acta Electronica Sinica, 2017,45(3): 730-739.
|
[9] |
DIRO A , CHILAMKURTI N . Distributed attack detection scheme using deep learning approach for Internet of Things[J]. Future Generation Computer Systems, 2018,82(1): 761-768.
|
[10] |
CHINCHORE R , SAMBARE S . Intrusion detection system by layered approach and hidden Markov model[J]. International Journal of Computer Application, 2015,5(2): 7-14.
|
[11] |
CHAWLA NV , BOWYER KW , HALL LO ,et al. SMOTE:synthetic minority over-sampling technique[J]. Journal of Artificial Intelligence Research, 2002,16(1): 321-357.
|
[12] |
HINTON G , SALAKHUTDINOV R . Reducing the dimensionality of data with neural networks[J]. Science, 2006,313(28): 504-507.
|
[13] |
徐彬, 陈渤, 刘宏伟 . 基于注意循环神经网络模型的雷达高分辨率距离像目标识别[J]. 电子与信息学报, 2016,38(12): 2988-2995.
|
|
XU B , CHEN B , LIU H W . Attention-based recurrent neural network model for radar high-resolution range prfile target recognition[J]// Journal of Electronics & Information Technology, 2016,38(12): 2988-2995.
|
[14] |
THANDA A , VENKATESAN S M . Audio visual speech recognition using deep recurrent neural networks[C]// IAPR Workshop on Multimodal Pattern Recognition of Social Signals in Human-Computer Interaction, 2016: 98-109.
|
[15] |
GUAMAN F , JOTY S , MARQUEZ L ,et al. Machine translation evaluation with neural networks[J]. Computer Speech & Language, 2017,45(1): 180-200.
|
[16] |
JORDAN MI , . Attractor dynamics and parallelism in connectionist sequential machine[C]// Eighth Conference of the Cognitive Science Society, 1986: 531-546.
|
[17] |
SONG J , TAKAKURA H , OKABE Y . Statistical analysis of honeypot data and building of Kyoto 2006+ dataset for NIDS evaluation[C]// The Workshop on Building Analysis Datasets & Gathering Experience Returns for Security. 2011: 29-36.
|
[18] |
TAVALLAEE M , BAGHERI E , LU W . A detailed analysis of the KDD CUP 99 data set[C]// IEEE International Conference on Computational Intelligence for Security and Defense Applications. 2009: 53-58.
|