%0 Conference Paper %1 Nguyen2019 %A Nguyen, Q. P. %A Lim, K. W. %A Divakaran, D. M. %A Low, K. H. %A Chan, M. C. %B Proc. IEEE Conf. Communications and Network Security (CNS) %D 2019 %K Anomaly Detection, Fingerprinting GEE, Gradient-based Internet, NetFlow Records, UGR VAE, analysis, anomalous anomaly attack autoencoder, dataset, deep-learning detection detection, explainable fingerprinting gradient gradient-based learning learning, machine methods, models, network recording, solutions, statistical technique, techniques, traffic unsupervised variational zero-day %P 91--99 %R 10.1109/CNS.2019.8802833 %T GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection

@inproceedings{Nguyen2019, added-at = {2020-10-15T14:36:56.000+0200}, author = {{Nguyen}, Q. P. and {Lim}, K. W. and {Divakaran}, D. M. and {Low}, K. H. and {Chan}, M. C.}, biburl = {https://www.bibsonomy.org/bibtex/222099e78366018a428f0d120fa662646/annakrause}, booktitle = {Proc. IEEE Conf. Communications and Network Security (CNS)}, doi = {10.1109/CNS.2019.8802833}, file = {:Nguyen_GEEAGradientBasedExplainableVariationalAutoencoderForNetworkAnomalyDetection.pdf:PDF}, interhash = {a2727e80d02f36d9fadb967865f4fb11}, intrahash = {22099e78366018a428f0d120fa662646}, issn = {null}, keywords = {Anomaly Detection, Fingerprinting GEE, Gradient-based Internet, NetFlow Records, UGR VAE, analysis, anomalous anomaly attack autoencoder, dataset, deep-learning detection detection, explainable fingerprinting gradient gradient-based learning learning, machine methods, models, network recording, solutions, statistical technique, techniques, traffic unsupervised variational zero-day}, month = jun, pages = {91--99}, timestamp = {2020-10-15T14:44:42.000+0200}, title = {{GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection}}, year = 2019 }