Abstract
In this work, a new encrypted traffic identification algorithm using sparse logistical regression and extreme learning machine (ELM) is introduced. The proposed method is based on randomness characteristics of encrypted traffic. we utilize ℓ1-norm regularized logistic regression to select sparse features. The identification is performed with the help of Extreme Learning Machine (ELM) because of its better identification and faster speed. In ELM, the input weights and the bias values are randomly chosen and the output weights are analytically calculated. Extensive experiments are performed using the proposed encrypted traffic identification algorithm and results are compared against state of the art techniques.
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Meng, J., Yang, L., Zhou, Y., Pan, Z. (2015). Encrypted Traffic Identification Based on Sparse Logistical Regression and Extreme Learning Machine. In: Cao, J., Mao, K., Cambria, E., Man, Z., Toh, KA. (eds) Proceedings of ELM-2014 Volume 2. Proceedings in Adaptation, Learning and Optimization, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-14066-7_7
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DOI: https://doi.org/10.1007/978-3-319-14066-7_7
Publisher Name: Springer, Cham
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