Deep Learning Techniques for Cyber Security Intrusion Detection : A Detailed Analysis

In this study, we present a detailed analysis of deep learning techniques for intrusion detection. Specifically, we analyze seven deep learning models, including, deep neural networks, recurrent neural networks, convolutional neural networks, restricted Boltzmann machine, deep belief networks, deep Boltzmann machines, and deep autoencoders. For each deep learning model, we study the performance of the model in binary classification and multiclass classification. We use the CSE-CIC-IDS 2018 dataset and TensorFlow system as the benchmark dataset and software library in intrusion detection experiments. In addition, we use the most important performance indicators, namely, accuracy, detection rate, and false alarm rate for evaluating the efficiency of several methods.

Content

Author and article information

Contributors

Mohamed Amine Ferrag

Leandros Maglaras

Helge Janicke

Richard Smith

Conference

Publication date: September 2019

Publication date (Print): September 2019

Pages: 126-136

Affiliations

[0001]Department of Computer Science

Guelma University, Algeria

[0002]School of Computer Science and Informatics

De Montfort University, UK

Article

DOI: 10.14236/ewic/icscsr19.16

SO-VID: d01bfab2-d796-41d9-83a3-06d0cb5ffc48

License:

This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Conference name: 6th International Symposium for ICS & SCADA Cyber Security Research 2019

Conference acronym: ICS-CSR

Conference number: 6

Conference location: Athens, Greece

Conference date: 10th-12th September 2019

Conference sponsor: Electronic Workshops in Computing (eWiC)

Conference theme: Cyber Security Research

History

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1477-9358 BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/icscsr19.16

Self URI (journal page): https://ewic.bcs.org/

REFERENCES

A. AbusittaM. BellaicheM. DagenaisT. Halabi 2019 A deep learning approach for proactive multi-cloud cooperative intrusion detection system Future Generation Computer Systems
A. AhmimM. DerdourM. A. Ferrag 2018 An intrusion detection system based on combining probability predictions of a tree of classifiers International Journal of Communication Systems 31 9 e3547
A. AhmimL. MaglarasM. A. FerragM. DerdourH. Janicke 2018 A novel hierarchical intrusion detection system based on decision tree and rules-based models arXiv preprint arXiv:1812.09059
T. AldwairiD. PereraM. A. Novotny 2018 An evaluation of the performance of restricted boltzmann machines as a model for anomaly network intrusion detection Computer Networks 144 111 119
M. Z. AlomV. BontupalliT. M. Taha 2015 Intrusion detection using deep belief networks 2015 National Aerospace and Electronics Conference (NAECON) 339 344 IEEE
K. AlrawashdehC. Purdy 2016 Toward an online anomaly intrusion detection system based on deep learning 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) 195 200 IEEE
S. BasumallikR. MaS. Eftekharnejad 2019 Packet-data anomaly detection in pmu-based state estimator using convolutional neural network International Journal of Electrical Power & Energy Systems 107 690 702
L. DengD. Yu 2014 Deep learning: methods and applications Foundations and Trends® in Signal Processing 7 3–4 197 387
Z. DewaL. A. Maglaras 2016 Data mining and intrusion detection systems International Journal of Advanced Computer Science and Applications 7 1 62 71
J. L. Elman 1990 Finding structure in time Cognitive science 14 2 179 211
F. FengX. LiuB. YongR. ZhouQ. Zhou 2019 Anomaly detection in ad-hoc networks based on deep learning model: A plug and play device Ad Hoc Networks 84 82 89
U. FioreF. PalmieriA. CastiglioneA. De Santis 2013 Network anomaly detection with the restricted boltzmann machine Neurocomputing 122 13 23
A. FischerC. Igel 2012 An introduction to restricted boltzmann machines iberoamerican congress on pattern recognition 14 36 Springer
K. FuD. ChengY. TuL. Zhang 2016 Credit card fraud detection using convolutional neural networks International Conference on Neural Information Processing 483 490 Springer
N. GaoL. GaoQ. GaoH. Wang 2014 An intrusion detection model based on deep belief networks 2014 Second International Conference on Advanced Cloud and Big Data 247 252 IEEE
G. GellyJ.-L. Gauvain 2017 Optimization of rnn-based speech activity detection IEEE/ACM Transactions on Audio, Speech, and Language Processing 26 3 646 656
J. GuZ. WangJ. KuenL. MaA. ShahroudyB. ShuaiT. LiuX. WangG. WangJ. Cai 2018 Recent advances in convolutional neural networks Pattern Recognition 77 354 377
K. HeX. ZhangS. RenJ. Sun 2016 Deep residual learning for image recognition Proceedings of the IEEE conference on computer vision and pattern recognition 770 778
G. E. Hinton 2009 Deep belief networks Scholarpedia 4 5 5947
H. JaegerH. Haas 2004 Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication science 304 5667 78 80
F. JiangY. FuB. B. GuptaF. LouS. RhoF. MengZ. Tian 2018 Deep learning based multi-channel intelligent attack detection for data security IEEE Transactions on Sustainable Computing
M. I. Jordan 1997 Serial order: A parallel distributed processing approach Advances in psychology 121 471 495 Elsevier
M.-J. KangJ.-W. Kang 2016 Intrusion detection system using deep neural network for in-vehicle network security PloS one 11 6 e0155781
S. M. KasongoY. Sun 2019 A deep learning method with filter based feature engineering for wireless intrusion detection system IEEE Access 7 38597 38607
F. A. KhanA. GumaeiA. DerhabA. Hussain 2019 Tsdl: A twostage deep learning model for efficient network intrusion detection IEEE Access
J. KimJ. KimH. L. T. ThuH. Kim 2016 Long short term memory recurrent neural network classifier for intrusion detection 2016 International Conference on Platform Technology and Service (PlatCon) 1 5 IEEE
W. LiuZ. WangX. LiuN. ZengY. LiuF. E. Alsaadi 2017 A survey of deep neural network architectures and their applications Neurocomputing 234 11 26
L. A. MaglarasK.-H. KimH. JanickeM. A. FerragS. RallisP. FragkouA. MaglarasT. J. Cruz 2018 Cyber security of critical infrastructures ICT Express 4 1 42 45
S. OtoumB. KantarciH. T. Mouftah 2019 On the feasibility of deep learning in sensor network intrusion detection IEEE Networking Letters
D. PapamartzivanosF. G. MármolG. Kambourakis 2019 Introducing deep learning self-adaptive misuse network intrusion detection systems IEEE Access 7 13546 13560
S. PotluriC. Diedrich 2016 Accelerated deep neural networks for enhanced intrusion detection system 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA) 1 8 IEEE
S. S. RoyA. MallikR. GulatiM. S. ObaidatP. Krishna 2017 A deep learning based artificial neural network approach for intrusion detection International Conference on Mathematics and Computing 44 53 Springer
R. and H. Larochelle Salakhutdinov 2010 Efficient learning of deep boltzmann machines Proceedings of the thirteenth international conference on artificial intelligence and statistics 693 700
M. A. SalamaH. F. EidR. A. RamadanA. DarwishA. E. Hassanien 2011 Hybrid intelligent intrusion detection scheme Soft computing in industrial applications 293 303 Springer
I. SharafaldinA. H. LashkariA. A. Ghorbani 2018 Toward generating a new intrusion detection dataset and intrusion traffic characterization ICISSP 108 116
N. ShoneT. N. NgocV. D. PhaiQ. Shi 2018 A deep learning approach to network intrusion detection IEEE Transactions on Emerging Topics in Computational Intelligence 2 1 41 50
B. StewartL. RosaL. A. MaglarasT. J. CruzM. A. FerragP. SimõesH. Janicke 2017 A novel intrusion detection mechanism for scada systems which automatically adapts to network topology changes EAI Endorsed Trans. Indust. Netw. & Intellig. Syst. 4 10 e4
C. SzegedyW. LiuY. JiaP. SermanetS. ReedD. AnguelovD. ErhanV. VanhouckeA. Rabinovich 2015 Going deeper with convolutions Proceedings of the IEEE conference on computer vision and pattern recognition 1 9
T. A. TangL. MhamdiD. McLernonS. A. R. ZaidiM. Ghogho 2016 Deep learning approach for network intrusion detection in software defined networking 2016 International Conference on Wireless Networks and Mobile Communications (WINCOM) 258 263 IEEE
T. A. TangL. MhamdiD. McLernonS. A. R. ZaidiM. Ghogho 2018 Deep recurrent neural network for intrusion detection in sdn-based networks 2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft) 202 206 IEEE
P. VincentH. LarochelleI. LajoieY. BengioP.-A. Manzagol 2010 Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion Journal of machine learning research 11 Dec 3371 3408
J. YangJ. DengS. LiY. Hao 2017 Improved traffic detection with support vector machine based on restricted boltzmann machine Soft Computing 21 11 3101 3112
C. YinY. ZhuJ. FeiX. He 2017 A deep learning approach for intrusion detection using recurrent neural networks Ieee Access 5 21954 21961
Y. YuJ. LongZ. Cai 2017 Network intrusion detection through stacking dilated convolutional autoencoders Security and Communication Networks 2017
M. D. ZeilerR. Fergus 2014 Visualizing and understanding convolutional networks European conference on computer vision 818 833 Springer
H. ZhangX. YuP. RenC. LuoG. Min 2019 Deep adversarial learning in intrusion detection: A data augmentation enhanced framework arXiv preprint arXiv:1901.07949
Z. ZhangX. ZhouX. ZhangL. WangP. Wang 2018 A model based on convolutional neural network for online transaction fraud detection Security and Communication Networks 2018
G. ZhaoC. ZhangL. Zheng 2017 Intrusion detection using deep belief network and probabilistic neural network 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC) 1 639 642 IEEE
L. ZhouX. OuyangH. YingL. HanY. ChengT. Zhang 2018 Cyber-attack classification in smart grid via deep neural network Proceedings of the 2nd International Conference on Computer Science and Application Engineering 90 ACM

Comments

Comment on this article

[1] A. AbusittaM. BellaicheM. DagenaisT. Halabi 2019 A deep learning approach for proactive multi-cloud cooperative intrusion detection system Future Generation Computer Systems

[2] A. AhmimM. DerdourM. A. Ferrag 2018 An intrusion detection system based on combining probability predictions of a tree of classifiers International Journal of Communication Systems 31 9 e3547

[3] A. AhmimL. MaglarasM. A. FerragM. DerdourH. Janicke 2018 A novel hierarchical intrusion detection system based on decision tree and rules-based models arXiv preprint arXiv:1812.09059

[4] T. AldwairiD. PereraM. A. Novotny 2018 An evaluation of the performance of restricted boltzmann machines as a model for anomaly network intrusion detection Computer Networks 144 111 119

[5] M. Z. AlomV. BontupalliT. M. Taha 2015 Intrusion detection using deep belief networks 2015 National Aerospace and Electronics Conference (NAECON) 339 344 IEEE

[6] K. AlrawashdehC. Purdy 2016 Toward an online anomaly intrusion detection system based on deep learning 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) 195 200 IEEE

[7] S. BasumallikR. MaS. Eftekharnejad 2019 Packet-data anomaly detection in pmu-based state estimator using convolutional neural network International Journal of Electrical Power & Energy Systems 107 690 702

[8] L. DengD. Yu 2014 Deep learning: methods and applications Foundations and Trends® in Signal Processing 7 3–4 197 387

[9] Z. DewaL. A. Maglaras 2016 Data mining and intrusion detection systems International Journal of Advanced Computer Science and Applications 7 1 62 71

[10] J. L. Elman 1990 Finding structure in time Cognitive science 14 2 179 211

[11] F. FengX. LiuB. YongR. ZhouQ. Zhou 2019 Anomaly detection in ad-hoc networks based on deep learning model: A plug and play device Ad Hoc Networks 84 82 89

[12] U. FioreF. PalmieriA. CastiglioneA. De Santis 2013 Network anomaly detection with the restricted boltzmann machine Neurocomputing 122 13 23

[13] A. FischerC. Igel 2012 An introduction to restricted boltzmann machines iberoamerican congress on pattern recognition 14 36 Springer

[14] K. FuD. ChengY. TuL. Zhang 2016 Credit card fraud detection using convolutional neural networks International Conference on Neural Information Processing 483 490 Springer

[15] N. GaoL. GaoQ. GaoH. Wang 2014 An intrusion detection model based on deep belief networks 2014 Second International Conference on Advanced Cloud and Big Data 247 252 IEEE

[16] G. GellyJ.-L. Gauvain 2017 Optimization of rnn-based speech activity detection IEEE/ACM Transactions on Audio, Speech, and Language Processing 26 3 646 656

[17] J. GuZ. WangJ. KuenL. MaA. ShahroudyB. ShuaiT. LiuX. WangG. WangJ. Cai 2018 Recent advances in convolutional neural networks Pattern Recognition 77 354 377

[18] K. HeX. ZhangS. RenJ. Sun 2016 Deep residual learning for image recognition Proceedings of the IEEE conference on computer vision and pattern recognition 770 778

[19] G. E. Hinton 2009 Deep belief networks Scholarpedia 4 5 5947

[20] H. JaegerH. Haas 2004 Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication science 304 5667 78 80

[21] F. JiangY. FuB. B. GuptaF. LouS. RhoF. MengZ. Tian 2018 Deep learning based multi-channel intelligent attack detection for data security IEEE Transactions on Sustainable Computing

[22] M. I. Jordan 1997 Serial order: A parallel distributed processing approach Advances in psychology 121 471 495 Elsevier

[23] M.-J. KangJ.-W. Kang 2016 Intrusion detection system using deep neural network for in-vehicle network security PloS one 11 6 e0155781

[24] S. M. KasongoY. Sun 2019 A deep learning method with filter based feature engineering for wireless intrusion detection system IEEE Access 7 38597 38607

[25] F. A. KhanA. GumaeiA. DerhabA. Hussain 2019 Tsdl: A twostage deep learning model for efficient network intrusion detection IEEE Access

[26] J. KimJ. KimH. L. T. ThuH. Kim 2016 Long short term memory recurrent neural network classifier for intrusion detection 2016 International Conference on Platform Technology and Service (PlatCon) 1 5 IEEE

[27] W. LiuZ. WangX. LiuN. ZengY. LiuF. E. Alsaadi 2017 A survey of deep neural network architectures and their applications Neurocomputing 234 11 26

[28] L. A. MaglarasK.-H. KimH. JanickeM. A. FerragS. RallisP. FragkouA. MaglarasT. J. Cruz 2018 Cyber security of critical infrastructures ICT Express 4 1 42 45

[29] S. OtoumB. KantarciH. T. Mouftah 2019 On the feasibility of deep learning in sensor network intrusion detection IEEE Networking Letters

[30] D. PapamartzivanosF. G. MármolG. Kambourakis 2019 Introducing deep learning self-adaptive misuse network intrusion detection systems IEEE Access 7 13546 13560

[31] S. PotluriC. Diedrich 2016 Accelerated deep neural networks for enhanced intrusion detection system 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA) 1 8 IEEE

[32] S. S. RoyA. MallikR. GulatiM. S. ObaidatP. Krishna 2017 A deep learning based artificial neural network approach for intrusion detection International Conference on Mathematics and Computing 44 53 Springer

[33] R. and H. Larochelle Salakhutdinov 2010 Efficient learning of deep boltzmann machines Proceedings of the thirteenth international conference on artificial intelligence and statistics 693 700

[34] M. A. SalamaH. F. EidR. A. RamadanA. DarwishA. E. Hassanien 2011 Hybrid intelligent intrusion detection scheme Soft computing in industrial applications 293 303 Springer

[35] I. SharafaldinA. H. LashkariA. A. Ghorbani 2018 Toward generating a new intrusion detection dataset and intrusion traffic characterization ICISSP 108 116

[36] N. ShoneT. N. NgocV. D. PhaiQ. Shi 2018 A deep learning approach to network intrusion detection IEEE Transactions on Emerging Topics in Computational Intelligence 2 1 41 50

[37] B. StewartL. RosaL. A. MaglarasT. J. CruzM. A. FerragP. SimõesH. Janicke 2017 A novel intrusion detection mechanism for scada systems which automatically adapts to network topology changes EAI Endorsed Trans. Indust. Netw. & Intellig. Syst. 4 10 e4

[38] C. SzegedyW. LiuY. JiaP. SermanetS. ReedD. AnguelovD. ErhanV. VanhouckeA. Rabinovich 2015 Going deeper with convolutions Proceedings of the IEEE conference on computer vision and pattern recognition 1 9

[39] T. A. TangL. MhamdiD. McLernonS. A. R. ZaidiM. Ghogho 2016 Deep learning approach for network intrusion detection in software defined networking 2016 International Conference on Wireless Networks and Mobile Communications (WINCOM) 258 263 IEEE

[40] T. A. TangL. MhamdiD. McLernonS. A. R. ZaidiM. Ghogho 2018 Deep recurrent neural network for intrusion detection in sdn-based networks 2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft) 202 206 IEEE

[41] P. VincentH. LarochelleI. LajoieY. BengioP.-A. Manzagol 2010 Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion Journal of machine learning research 11 Dec 3371 3408

[42] J. YangJ. DengS. LiY. Hao 2017 Improved traffic detection with support vector machine based on restricted boltzmann machine Soft Computing 21 11 3101 3112

[43] C. YinY. ZhuJ. FeiX. He 2017 A deep learning approach for intrusion detection using recurrent neural networks Ieee Access 5 21954 21961

[44] Y. YuJ. LongZ. Cai 2017 Network intrusion detection through stacking dilated convolutional autoencoders Security and Communication Networks 2017

[45] M. D. ZeilerR. Fergus 2014 Visualizing and understanding convolutional networks European conference on computer vision 818 833 Springer

[46] H. ZhangX. YuP. RenC. LuoG. Min 2019 Deep adversarial learning in intrusion detection: A data augmentation enhanced framework arXiv preprint arXiv:1901.07949

[47] Z. ZhangX. ZhouX. ZhangL. WangP. Wang 2018 A model based on convolutional neural network for online transaction fraud detection Security and Communication Networks 2018

[48] G. ZhaoC. ZhangL. Zheng 2017 Intrusion detection using deep belief network and probabilistic neural network 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC) 1 639 642 IEEE

[49] L. ZhouX. OuyangH. YingL. HanY. ChengT. Zhang 2018 Cyber-attack classification in smart grid via deep neural network Proceedings of the 2nd International Conference on Computer Science and Application Engineering 90 ACM

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