Variational data generative model for intrusion detection
暂无分享,去创建一个
[1] Zhiting Hu,et al. Improved Variational Autoencoders for Text Modeling using Dilated Convolutions , 2017, ICML.
[2] Ben Poole,et al. Categorical Reparameterization with Gumbel-Softmax , 2016, ICLR.
[3] Phil Blunsom,et al. Neural Variational Inference for Text Processing , 2015, ICML.
[4] Anamika Yadav,et al. Performance analysis of NSL-KDD dataset using ANN , 2015, 2015 International Conference on Signal Processing and Communication Engineering Systems.
[5] Alex Graves,et al. DRAW: A Recurrent Neural Network For Image Generation , 2015, ICML.
[6] Probal Chaudhuri,et al. Comparison of multivariate distributions using quantile-quantile plots and related tests , 2014, 1407.1212.
[7] Jugal K. Kalita,et al. Network Anomaly Detection: Methods, Systems and Tools , 2014, IEEE Communications Surveys & Tutorials.
[8] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[9] Mahmod S. Mahmod,et al. A COMPARISON STUDY FOR INTRUSION DATABASE (KDD99, NSL-KDD) BASED ON SELF ORGANIZATION MAP (SOM) ARTIFICIAL NEURAL NETWORK , 2013 .
[10] Francisco Herrera,et al. A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[11] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[12] Vern Paxson,et al. Outside the Closed World: On Using Machine Learning for Network Intrusion Detection , 2010, 2010 IEEE Symposium on Security and Privacy.
[13] Hien M. Nguyen,et al. Borderline over-sampling for imbalanced data classification , 2009, Int. J. Knowl. Eng. Soft Data Paradigms.
[14] Ali A. Ghorbani,et al. A detailed analysis of the KDD CUP 99 data set , 2009, 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications.
[15] Haibo He,et al. ADASYN: Adaptive synthetic sampling approach for imbalanced learning , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).
[16] David A. Cieslak,et al. Combating imbalance in network intrusion datasets , 2006, 2006 IEEE International Conference on Granular Computing.
[17] Hui Han,et al. Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning , 2005, ICIC.
[18] John W. Fisher,et al. Estimating dependency and significance for high-dimensional data , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..
[19] Gary M. Weiss. Mining with rarity: a unifying framework , 2004, SKDD.
[20] Gustavo E. A. P. A. Batista,et al. A study of the behavior of several methods for balancing machine learning training data , 2004, SKDD.
[21] Samy Bengio,et al. Taking on the curse of dimensionality in joint distributions using neural networks , 2000, IEEE Trans. Neural Networks Learn. Syst..
[22] R. Zamar,et al. A multivariate Kolmogorov-Smirnov test of goodness of fit , 1997 .
[23] Geoffrey E. Hinton,et al. Autoencoders, Minimum Description Length and Helmholtz Free Energy , 1993, NIPS.
[24] Murray D. Burke,et al. On the multivariate two-sample problem using strong approximations of the EDF , 1977 .
[25] Seetha Hari,et al. Learning From Imbalanced Data , 2019, Advances in Computer and Electrical Engineering.
[26] Sungzoon Cho,et al. Variational Autoencoder based Anomaly Detection using Reconstruction Probability , 2015 .
[27] Edwin de Jonge,et al. Visualizing and Inspecting Large Datasets with Tableplots , 2013, Journal of Data Science.
[28] Zhi-Hua Zhou,et al. Exploratory Undersampling for Class-Imbalance Learning , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[29] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..