Hybrid Deep Learning for Botnet Attack Detection in the Internet-of-Things Networks
作者： Segun I. Popoola , Bamidele Adebisi , Mohammad Hammoudeh , Guan Gui , Haris Gacanin
DOI: 10.1109/JIOT.2020.3034156
关键词:
摘要: Deep learning (DL) is an efficient method for botnet attack detection. However, the volume of network traffic data and memory space required is usually large. It is, therefore, almost …
mmu.ac.uk 
sci-hub.st 参考文章(80)
Matthew D. Zeiler, ADADELTA: An Adaptive Learning Rate Method arXiv: Learning. ,(2012)
Prasanta Gogoi, Monowar H. Bhuyan, D. K. Bhattacharyya, J. K. Kalita, Packet and Flow Based Network Intrusion Dataset international conference on contemporary computing. pp. 322- 334 ,(2012) , 10.1007/978-3-642-32129-0_34
Ilya Sutskever, Geoffrey Hinton, James Martens, George Dahl, On the importance of initialization and momentum in deep learning international conference on machine learning. pp. 1139- 1147 ,(2013)
Bahareh Abolhasanzadeh, Nonlinear dimensionality reduction for intrusion detection using auto-encoder bottleneck features conference on information and knowledge technology. pp. 1- 5 ,(2015) , 10.1109/IKT.2015.7288799
Sajal Bhatia, Desmond Schmidt, George Mohay, Alan Tickle, A framework for generating realistic traffic for Distributed Denial-of-Service attacks and Flash Events Computers & Security. ,vol. 40, pp. 95- 107 ,(2014) , 10.1016/J.COSE.2013.11.005
Carl Livadas, Robert Walsh, David Lapsley, W. Timothy Strayer, Usilng Machine Learning Technliques to Identify Botnet Traffic local computer networks. pp. 967- 974 ,(2006) , 10.1109/LCN.2006.322210
Sepp Hochreiter, Jürgen Schmidhuber, Long short-term memory Neural Computation. ,vol. 9, pp. 1735- 1780 ,(1997) , 10.1162/NECO.1997.9.8.1735
Yoshua Bengio, Learning Deep Architectures for AI ,(2009)
H. Brendan McMahan, Gary Holt, D. Sculley, Michael Young, Dietmar Ebner, Julian Grady, Lan Nie, Todd Phillips, Eugene Davydov, Daniel Golovin, Sharat Chikkerur, Dan Liu, Martin Wattenberg, Arnar Mar Hrafnkelsson, Tom Boulos, Jeremy Kubica, Ad click prediction: a view from the trenches knowledge discovery and data mining. pp. 1222- 1230 ,(2013) , 10.1145/2487575.2488200
Svante Wold, Kim Esbensen, Paul Geladi, Principal component analysis Chemometrics and Intelligent Laboratory Systems. ,vol. 2, pp. 37- 52 ,(1987) , 10.1016/0169-7439(87)80084-9