A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks

10.1016/j.asoc.2014.01.028

10.1109/ICACCI.2016.7732199

10.1155/2014/240217

10.1109/SPACES.2015.7058223

10.1016/j.procs.2016.06.047

10.1109/TSMCC.2008.923876

khan, 2016, A survey on intrusion detection systems and classification techniques, Int J Sci Res Sci Eng Technol, 2, 202

10.1109/COMST.2015.2494502

10.4108/eai.3-12-2015.2262516

10.1109/WINCOM.2016.7777224

2016, Weka 3 Data mining with open source machine learning software in Java

10.1109/TPAMI.2016.2537337

2017, Welcome Theano 0 9 0 Documentation

10.1109/TCYB.2015.2399172

karpathy, 2015, The Unreasonable Effectiveness of Recurrent Neural Networks[R]

10.1109/TCYB.2015.2493538

10.1109/TCYB.2014.2347057

10.1016/j.cviu.2016.03.013

10.1016/j.neunet.2014.09.003

10.1016/j.imavis.2016.04.011

10.1038/nature14539

10.1007/s00521-010-0487-0

revathi, 2013, A detailed analysis on NSL-KDD dataset using various machine learning techniques for intrusion detection, Int J Eng Res Technol, 2, 1848

10.1109/CISDA.2009.5356528

bhattacharjee, 2017, Intrusion detection system for NSL-KDD data set using vectorised fitness function in genetic algorithm, Advances in Computer Science and Technology, 10, 235

10.1109/eStream.2017.7950325

martens, 0, Learning recurrent neural networks with hessian-free optimization, 1033

10.1016/j.ins.2016.04.019