Abstract
Classification of network traffic for intrusion detection is a Big Data classification problem. It requires an efficient Machine Learning technique to learn the characteristics of the rapidly changing varieties of traffic in large volume and high velocity so that this knowledge can be applied to a classification task. This paper proposes a supervised-learning technique called the Unit Ring Machine which utilizes the geometric patterns of the network traffic variables to learn the traffic characteristics. It provides a single-domain, representation-learning technique with a class-separate objective for the network intrusion detection. It assigns a large volume of network traffic data to a single unit-ring and categorizes them based on the varieties of network traffic, making it a highly suitable technique for the Big Data classification of network intrusion traffic.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Laskov, P., Dussel, P., Schafer, C., Rieck, K.: Learning intrusion detection: supervised or unsupervised? In: Proceedings of the 13th ICIAP Conference, pp. 50–57 (2005)
Kotsiantis, S.B.: Supervised machine learning: A review of classification techniques. Informatica 31, 249–268 (2007)
White, T.: Hadoop: The Definitive Guide, 3rd edn. O’ Reilly Media Inc. (2012)
Bengio, Y., Courville, A., Vincentar, P.: Representation Learning: A Review and New Perspectives. arXiv:1206.5538v2 [cs.LG] (2012)
Tu, W., Sun, S.: Cross-domain representation-learning framework with combination of class-separate and domain-merge objectives. In: Proceedings of the CDKD 2012 Conference, pp. 18–25 (2012)
Suthaharan, S.: A unit-circle classification algorithm to characterize back attack and normal traffic for intrusion detection. In: Proc. of the IEEE International Conference on Intelligence and Security Informatics, pp. 150–152 (2012)
Laskov, P., Schafer, C., Kotenko, I.: Intrusion detection in unlabeled data with quarter-sphere support vector machines. In: Proceedings of the DIMVA Conference, pp. 71–82 (2004)
Huang, G., Chen, H., Zhou, Z., Yin, F., Guo, K.: Two-class support vector data description. Pattern Recognition 44, 320–329 (2011)
Corona, I., Giacinto, G., Roli, F.: Intrusion detection in computer systems using multiple classifier systems. Studies in Computational Intelligence (SCI) 126, 91–113 (2008)
Giacinto, G., Perdisci, R., Roli, F.: Network intrusion detection by combining one-class classifier. In: Roli, F., Vitulano, S. (eds.) ICIAP 2005. LNCS, vol. 3617, pp. 58–65. Springer, Heidelberg (2005)
Mangasarian, O.L., Musicant, D.R.: Lagrangian support vector machine classification. TR 00-06, Data Mining Institute, Department of Computer Science, University of Wisconsin, USA (2000), ftp://ftp.cs.wisc.edu/pub/dmi/tech-reports/00-06.pdf
Jeyakumar, V., Li, G., Suthaharan, S.: Support vector machine classifiers with uncertain knowledge sets via robust convex optimization. Optimization the Journal of Mathematical Programming and Operations Research, 1–18 (2012)
Chen, Y., Li, Y., Cheng, X., Guo, L.: Survey and taxonomy of feature selection algorithms in intrusion detection system. In: Lipmaa, H., Yung, M., Lin, D. (eds.) Inscrypt 2006. LNCS, vol. 4318, pp. 153–167. Springer, Heidelberg (2006)
Kayacik, H.G., Zincir-Heywood, A.N., Heywoo, M.I.: Selecting features for intrusion detection: A feature relevance analysis on KDD 99 Intrusion Detection Datasets. Association of Computer Machinery Press, 85–89 (2006)
Li, Y., Wang, J., Tian, Z., Lu, T., Young, C.: Building lightweight intrusion detection system using wrapper-based feature selection mechanisms. Computers and Security 28(6), 466–475 (2009)
NSL-KDD, http://www.iscx.ca/NSL-KDD/
Hastie, T., Tibshirani, R., Friedman, J.H.: The Elements of Statistical Learning: Data mining, Inference, and Prediction. Springer, New York (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Suthaharan, S. (2013). A Single-Domain, Representation-Learning Model for Big Data Classification of Network Intrusion. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2013. Lecture Notes in Computer Science(), vol 7988. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39712-7_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-39712-7_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39711-0
Online ISBN: 978-3-642-39712-7
eBook Packages: Computer ScienceComputer Science (R0)