Abstract
Detecting encrypted traffic is increasingly important for deep packet inspection (DPI) to improve the performance of intrusion detection systems. We propose a machine learning approach with several randomness tests to achieve high accuracy detection of encrypted traffic while requiring low overhead incurred by the detection procedure. To demonstrate how effective the proposed approach is, the performance of four classification methods (Naïve Bayesian, Support Vector Machine, CART and AdaBoost) are explored. Our recommendation is to use CART which is not only capable of achieving an accuracy of 99.9% but also up to about 2.9 times more efficient than the second best candidate (Naïve Bayesian).
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Acknowledgements
This research was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. B0717-16-0116, Development of information leakage prevention and ID management for secure drone services).
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (No. 2014R1A1A1003707).
This research was supported by the MSIP(Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2016-R0992-16-1006) supervised by the IITP (Institute for Information & communications Technology Promotion).
This work was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIP) (No.R-20160222-002755, Cloud based Security Intelligence Technology Development for the Customized Security Service Provisioning).
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Cha, S., Kim, H. (2017). Detecting Encrypted Traffic: A Machine Learning Approach. In: Choi, D., Guilley, S. (eds) Information Security Applications. WISA 2016. Lecture Notes in Computer Science(), vol 10144. Springer, Cham. https://doi.org/10.1007/978-3-319-56549-1_5
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DOI: https://doi.org/10.1007/978-3-319-56549-1_5
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