Abstract
Network traffic classification is important to many network applications. Machine learning is regarded as one of the most effective technique to classify network traffic. In this paper, we adopt the fast correlation-based filter algorithm to filter redundant attributes contained in network traffic. The attributes selected by this algorithm help to reduce the classification complexity and achieve high classification accuracy. Since the traffic attributes contain a large amount of users’ behavior information, the privacy of user may be revealed and illegally used by malicious users. So it’s demanding to classify traffic with certain segment of frames which encloses privacy-related information being protected. After classification, the results do not disclose privacy information, while may still be used for data analysis. Therefore, we propose a random perturbation algorithm based on relationship among different data attributes’ orders, which protects their privacy, thus ensures data security during classification. The experiment results demonstrate that data perturbed by our algorithm is classified with high accuracy rate and data utility.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Guo, L., Shen, H.: Privacy-preserving internet traffic publication. In: IEEE Trustcom/BigDataSE/ISPA, pp. 884–891 (2017)
Moore, A.W., Papagiannaki, K.: Toward the accurate identification of network applications. In: Dovrolis, C. (ed.) PAM 2005. LNCS, vol. 3431, pp. 41–54. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-31966-5_4
Madhukar, A., Williamson, C.: A longitudinal study of P2P traffic classification. In: 14th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, pp. 179–188 (2006)
Kanungo, T., Mount, D.M., Netanyahu, N.S.: An efficient K-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24, 881–892 (2002)
McGregor, A., Hall, M., Lorier, P., Brunskill, J.: Flow clustering using machine learning techniques. In: Barakat, C., Pratt, I. (eds.) PAM 2004. LNCS, vol. 3015, pp. 205–214. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24668-8_21
Zander, S., Nguyen, T., Armitage, G.: Automated traffic classification and application identification using machine learning. In: IEEE Conference on Local Computer Networks, pp. 250–257 (2005)
Erman, J., Arlitt, M., Mahanti, A.: Traffic classification using clustering algorithms. In: Proceedings of the 2006 SIGCOMM Workshop on Mining Network Data, pp. 281–286 (2006)
Moore, A.W., Zuev, D.: Internet traffic classification using bayesian analysis techniques. ACM SIGMETRICS Perform. Eval. Rev. 33, 50–60 (2005)
Williams, N., Zander, S.: Evaluating machine learning algorithms for automated network application identification, Center for Advanced Internet Architectures Technical report (2006)
Li, W., Moore, A.W.: A machine learning approach for efficient traffic classification. In: 15th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, pp. 310–317 (2007)
Deng, H., Yang, A.M.: P2P traffic classification method based on SVM. In: Computer Engineering and Applications (2006)
Aggarwal, C.C.: On k-anonymity and the curse of dimensionality. In: Proceedings of the 31st International Conference on Very Large Data Bases, pp. 901–909 (2005)
Waters, B.: Efficient identity-based encryption without random oracles. In: Cramer, R. (ed.) EUROCRYPT 2005. LNCS, vol. 3494, pp. 114–127. Springer, Heidelberg (2005). https://doi.org/10.1007/11426639_7
Dwork, C.: Differential Privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006, Part II. LNCS, vol. 4052, pp. 1–12. Springer, Heidelberg (2006). https://doi.org/10.1007/11787006_1
Moore, A.W., Zuev, D.: Discriminators for use in ow-based classification (2005)
Yu, L., Liu, H.: Feature selection for high-dimensional data: a fast correlation-based filter solution. In: 20th International Conference on Machine Learning, pp. 856–863 (2003)
Acknowledgement
This work was done under the support of Research Initiative Grant of Australian Research Council Discovery Projects funding DP150104871, Beijing Natural Science Foundation Grant No. 4172045 and National Science Foundation of China Grant No. 61501025.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Lu, Y., Tian, H., Shen, H., Xu, D. (2019). Privacy Preserving Classification Based on Perturbation for Network Traffic. In: Park, J., Shen, H., Sung, Y., Tian, H. (eds) Parallel and Distributed Computing, Applications and Technologies. PDCAT 2018. Communications in Computer and Information Science, vol 931. Springer, Singapore. https://doi.org/10.1007/978-981-13-5907-1_13
Download citation
DOI: https://doi.org/10.1007/978-981-13-5907-1_13
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-5906-4
Online ISBN: 978-981-13-5907-1
eBook Packages: Computer ScienceComputer Science (R0)