Browse > Article
http://dx.doi.org/10.15207/JKCS.2019.10.11.001

Traffic Data Generation Technique for Improving Network Attack Detection Using Deep Learning  

Lee, Wooho (Interdisciplinary Program of Information Security, Chonnam National University)
Hahm, Jaegyoon (Div. of National Supercomputing, Korea Institute of Science and Technology Information)
Jung, Hyun Mi (Div. of National Supercomputing, Korea Institute of Science and Technology Information)
Jeong, Kimoon (Div. of National Supercomputing, Korea Institute of Science and Technology Information)
Publication Information
Journal of the Korea Convergence Society / v.10, no.11, 2019 , pp. 1-7 More about this Journal
Abstract
Recently, various approaches to detect network attacks using machine learning have been studied and are being applied to detect new attacks and to increase precision. However, the machine learning method is dependent on feature extraction and takes a long time and complexity. It also has limitation of performace due to learning data imbalance. In this study, we propose a method to solve the degradation of classification performance due to imbalance of learning data among the limit points of detection system. To do this, we generate data using Generative Adversarial Networks (GANs) and propose a classification method using Convolutional Neural Networks (CNNs). Through this approach, we can confirm that the accuracy is improved when applied to the NSL-KDD and UNSW-NB15 datasets.
Keywords
Network security; Intrusion detection; Network traffic data; Deep learning; GAN;
Citations & Related Records
연도 인용수 순위
  • Reference
1 K. Wang & S.J. Stolfo. (2004, September). Anomalous payload-based network intrusion detection. RAID. (pp. 203-222). Berlin : Springer.
2 N. Williams, S. Zander & G. Armitage. (2006). A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification. ACM SIGCOMM Compute Commun, Rev, 36(5), 5-16.   DOI
3 UCI KDD Archive. (2005) kdd aRCHIVE. KDDcup99 dataset. KDD [Online]. https://kdd.ics.uci.edu/databases/kddcup99/task.html
4 L. Dhanabal & S. P. Shantharajah. (2015). A Study on NSL-KDD Dataset for Intrusion Detection System Based on Classification Algorithms. International Journal of Advanced Research in Computer and Engineering, 4(6), 446-452.
5 N. V. Chawla et al. (2002). SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research. 16, 321-357.   DOI
6 S. Hu et al. (2009). MSMOTE: Improving classification performance when training data is imbalanced. 2009 Second international workshop on computer science and engineering, (2, pp.13-17). IEEE.
7 L. Yu et al. (2017). Seqgan: Sequence generative adversarial nets with policy gradient. Thirty-First AAAI Conference on Artificial Intelligence.
8 N. Moustafa & J. Slay. (2015). UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). Military communications and information systems conference(MilCIS), IEEE.
9 B. Dong & X. Wang. (2016). Comparison deep learning method to traditional methods using for network intrusion detection. 2016 8th IEEE International Conference on Communication Software and Networks(ICCSN), (pp.581-585). IEEE.
10 M. Lopez-Martin, B. Carro, A. Sanchez-Esguevillas & J. Lloret. (2017). Network traffic classifier with convolutional and recurrent neural networks for internet of things. IEEE Access, 5, 18042-18050.   DOI
11 R. K. Rahul et al. (2017). Deep learning for network flow analysis and malware classification. International Symposium on Security in Computing and Communication. Singapore : Springer.
12 T. Auld, A. W. Moore & S. F. Gull. (2007). Bayesian neural networks for internet traffic classification. IEEE Transactions on Neural Networks, 18(1), 223-239.   DOI
13 W. WANG et al. (2017). Malware traffic classification using convolutional neural network for representation learning. 2017 International Conference on Information Networking(ICOIN), (pp. 712-717). IEEE.
14 T. Mikolov, K. Chen, G. Corrado & J. Dean. (2013). Efficient estimation of word representations in vector space. arXiv preprint.
15 V. Nair & G. E. Hinton (2010). Rectified linear units improve restricted boltzmann machines. Proceedings of the 27th International Conference on Machine Learning(ICML-10), (pp. 807-814).
16 X. Zhang, J. Zhao & Y. LeCun. (2015). Character-level convolutional networks for text classification. Advances in neural information processing systems. (pp. 649-657).
17 Z. Zivkovic. (2004, August). Improved adaptive Gaussian mixture model for background subtraction. ICPR, (2, pp. 28-31), IEEE.