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A Deep Learning Approach for Network Intrusion Detection System

Authors:

Mansoor AlamAuthors Info & Claims

BICT'15: Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS)

Pages 21 - 26

https://doi.org/10.4108/eai.3-12-2015.2262516

Published: 24 May 2016 Publication History

Abstract

A Network Intrusion Detection System (NIDS) helps system administrators to detect network security breaches in

their organizations. However, many challenges arise while

developing a flexible and efficient NIDS for unforeseen and unpredictable attacks. We propose a deep learning based approach for developing such an efficient and flexible NIDS.

We use Self-taught Learning (STL), a deep learning based technique, on NSL-KDD - a benchmark dataset for network

intrusion. We present the performance of our approach and compare it with a few previous work. Compared metrics include accuracy, precision, recall, and f-measure values.

References

[1]

Snort, "https://www.snort.org/."

[2]

U. Fiore, F. Palmieri, A. Castiglione, and A. De Santis, "Network Anomaly Detection with the Restricted Boltzmann Machine," Neurocomput., vol. 122, pp. 13-23, 2013.

[3]

C. F. Tsai, Y. F. Hsu, C. Y. Lin, and W. Y. Lin, "Intrusion Detection by Machine Learning: A Review," Expert Systems with Applications, vol. 36, no. 10, pp. 11994-12000, 2009.

[4]

R. Raina, A. Battle, H. Lee, B. Packer, and A. Y. Ng, "Self-taught Learning: Transfer Learning from Unlabeled Data," in Proceedings of the 24th International Conference on Machine Learning, ICML '07, (New York, NY, USA), pp. 759-766, 2007.

[5]

M. A. Salama, H. F. Eid, R. A. Ramadan, A. Darwish, and A. E. Hassanien, "Hybrid Intelligent Intrusion Detection Scheme," in Soft computing in industrial applications, pp. 293-303, Springer, 2011.

[6]

R. S. Naoum, N. A. Abid, and Z. N. Al-Sultani, "An Enhanced Resilient Backpropagation Artificial Neural Network for Intrusion Detection System," International Journal of Computer Science and Network Security, vol. 12, no. 3, pp. 11-16, 2012.

[7]

H. S. Chae, B. O. Jo, S. H. Choi, and T. K. Park, "Feature Selection for Intrusion Detection using NSL-KDD," Recent Advances in Computer Science, pp. 184-187, 2013.

[8]

S. Thaseen and C. A. Kumar, "An Analysis of Supervised Tree based Classifiers for Intrusion Detection System," in Pattern Recognition, Informatics and Mobile Engineering (PRIME), 2013 International Conference on, pp. 294-299, IEEE, 2013.

[9]

M. Panda, A. Abraham, and M. R. Patra, "Discriminative Multinomial Naive Bayes for Network Intrusion Detection," in Information Assurance and Security (IAS), 2010 Sixth International Conference on, pp. 5-10, IEEE, 2010.

[10]

M. Panda, A. Abraham, and M. R. Patra, "A Hybrid Intelligent Approach for Network Intrusion Detection," Procedia Engineering, vol. 30, pp. 1-9, 2012.

[11]

H. F. Eid, A. Darwish, A. E. Hassanien, and A. Abraham, "Principle Components Analysis and Support Vector Machine based Intrusion Detection System," in Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on, pp. 363-367, IEEE, 2010.

[12]

H. F. Eid, M. A. Salama, A. E. Hassanien, and T. H. Kim, "Bi-layer Behavioral-based Feature Selection Approach for Network Intrusion Classi_cation," in Security Technology, pp. 195-203, Springer, 2011.

[13]

P. Krömer, J. Plato¿, V. Snáael, and A. Abraham, "Fuzzy Classi_cation by Evolutionary Algorithms," in Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on, pp. 313-318, IEEE, 2011.

[14]

I. Syarif, A. Prugel-Bennett, and G. Wills, "Unsupervised Clustering Approach for Network Anomaly Detection," in Networked Digital Technologies, pp. 135-145, Springer, 2012.

[15]

P. Gogoi, M. H. Bhuyan, D. Bhattacharyya, and J. K. Kalita, "Packet and Flow based Network Intrusion Dataset," in Contemporary Computing, pp. 322-334, Springer, 2012.

[16]

C. R. Pereira, R. Y. Nakamura, K. A. Costa, and J. P. Papa, "An Optimum-Path Forest Framework for Intrusion Detection in Computer Networks," Engineering Applications of Artificial Intelligence, vol. 25, no. 6, pp. 1226-1234, 2012.

[17]

Z. Wang, "The Applications of Deep Learning on Traffic Identification." https://goo.gl/WouIM6.

[18]

A. Ng, "Sparse Autoencoder," 2011.

[19]

A. Coates, A. Y. Ng, and H. Lee, "An Analysis of Single-layer Networks in Unsupervised Feature Learning," in International conference on artificial intelligence and statistics, pp. 215-223, 2011.

[20]

M. Tavallaee, E. Bagheri, W. Lu, and A. Ghorbani, "A Detailed Analysis of the KDD CUP 99 Data Set," in Computational Intelligence for Security and Defense Applications, 2009. CISDA 2009. IEEE Symposium on, pp. 1-6, July 2009.

[21]

Soft-Max Regression, "http://udl.stanford.edu/wiki/index.php/softmax_regression."

[22]

KDD Cup 99, "http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html."

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Zamanzadeh Darban ZWebb GPan SAggarwal CSalehi M(2024)Deep Learning for Time Series Anomaly Detection: A SurveyACM Computing Surveys10.1145/369133857:1(1-42)Online publication date: 7-Oct-2024
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Liu LSun TYang LBai J(2024)TAM-CNN: An Enhanced Malicious Encrypted Traffic Detection Method with Feature-Threshold ImagesProceedings of the ACM Turing Award Celebration Conference - China 202410.1145/3674399.3674458(171-176)Online publication date: 5-Jul-2024
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A Deep Learning Approach for Network Intrusion Detection System
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning

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Published In

cover image Guide Proceedings

BICT'15: Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS)

May 2016

618 pages

ISBN:9781631901003

Editors:
Junichi Suzuki
Associate Professor, Dept of Comp. Science, University of Massachusetts, Boston, 10 Morrissey Blvd., Boston, MA 02125, USA
,
Tadashi Nakano
Associate Professor, Osaka University, 1-3 Yamadaoka, Suita, Osaka 565-0871, Japan
,
Henry Hess
Associate Professor, Department of Biomedical Engineering, Columbia University, 351L Engineering Terrace, MC 8904, 1210 Amsterdam Avenue, New York, NY 10027

Publisher

ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)

Brussels, Belgium

Publication History

Published: 24 May 2016

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Cited By

Zamanzadeh Darban ZWebb GPan SAggarwal CSalehi M(2024)Deep Learning for Time Series Anomaly Detection: A SurveyACM Computing Surveys10.1145/369133857:1(1-42)Online publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1145/3691338
Liu LSun TYang LBai J(2024)TAM-CNN: An Enhanced Malicious Encrypted Traffic Detection Method with Feature-Threshold ImagesProceedings of the ACM Turing Award Celebration Conference - China 202410.1145/3674399.3674458(171-176)Online publication date: 5-Jul-2024
https://dl.acm.org/doi/10.1145/3674399.3674458
Wang YYan R(2024)Pixel Power: Harnessing Image Processing for Optimizing Few-Shot Malicious Traffic DetectionProceedings of the 2024 2nd Asia Conference on Computer Vision, Image Processing and Pattern Recognition10.1145/3663976.3664005(1-6)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3663976.3664005
Fu XJiang CLi CLi JZhu XLi F(2024)A hybrid approach for Android malware detection using improved multi-scale convolutional neural networks and residual networksExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.123675249:PBOnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.123675
Wang YWang J(2023)Elastic Detection Mechanism Aimed at Hybrid DDoS AttackProceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning10.1145/3590003.3590031(154-160)Online publication date: 17-Mar-2023
https://dl.acm.org/doi/10.1145/3590003.3590031
Apruzzese GLaskov PMontes de Oca EMallouli WBrdalo Rapa LGrammatopoulos ADi Franco F(2023)The Role of Machine Learning in CybersecurityDigital Threats: Research and Practice10.1145/35455744:1(1-38)Online publication date: 7-Mar-2023
https://dl.acm.org/doi/10.1145/3545574
Soltani MOusat BJafari Siavoshani MJahangir A(2023)An adaptable deep learning-based intrusion detection system to zero-day attacksJournal of Information Security and Applications10.1016/j.jisa.2023.10351676:COnline publication date: 24-Aug-2023
https://dl.acm.org/doi/10.1016/j.jisa.2023.103516
Abdelkhalek AMashaly M(2023)Addressing the class imbalance problem in network intrusion detection systems using data resampling and deep learningThe Journal of Supercomputing10.1007/s11227-023-05073-x79:10(10611-10644)Online publication date: 12-Feb-2023
https://dl.acm.org/doi/10.1007/s11227-023-05073-x
Gao J(2022)Network Intrusion Detection Method Combining CNN and BiLSTM in Cloud Computing EnvironmentComputational Intelligence and Neuroscience10.1155/2022/72724792022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/7272479
Srivastava JRoutray SAhmad SWaris M(2022)Internet of Medical Things (IoMT)-Based Smart Healthcare SystemComputational Intelligence and Neuroscience10.1155/2022/72181132022Online publication date: 16-Jul-2022
https://dl.acm.org/doi/10.1155/2022/7218113
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