| Analyzing HTTPS encrypted traffic to identify user's operating system, browser and application J Muehlstein, Y Zion, M Bahumi, I Kirshenboim, R Dubin, A Dvir, O Pele 2017 14th IEEE Annual Consumer Communications & Networking Conference (CCNC …, 2017 | 77 | 2017 |
| Analyzing https encrypted traffic to identify user’s operating system, browser and application R Dubin, A Dvir, O Pele, J Muehlstein, Y Zion, M Bahumi, I Kirshenboim IEEE Consumer Communications and Networking Conference. IEEE, 2017 | 8 | 2017 |
| Robust machine learning for encrypted traffic classification A Dvir, Y Zion, J Muehlstein, O Pele, C Hajaj, R Dubin arXiv preprint arXiv:1603.04865, 2016 | 4 | 2016 |
| Analyzing HTTPS Traffic for a Robust Identification of Operating System, Browser and Application J Muehlstein, Y Zion, M Bahumi, I Kirshenboim, R Dubin, A Dvir, O Pele arXiv, arXiv: 1603.04865, 2016 | 3 | 2016 |
| Analyzing https traffic for a robust identification of operating system, browser and application Y Zion, J Muehlstein, M Bahumi, I Kirshenboim, R Dubin, A Dvir, O Pele arXiv preprint arXiv:1603.04865, 2016 | 2 | 2016 |
| Classification and enrichment of encrypted traffic using machine learning algorithms Y Zion Master dissertation, Ariel University, Israel, 2018 | 1 | 2018 |
| Revolutionizing Our Way to Better Classifiers: Leveraging Synthetic Data with Generative Models for Encrypted Network Traffic Classification Y Zion, C Hajaj, A Dvir, G Ben-Artzi, S Mahpod, R Dubin Available at SSRN 4654236, 0 | | |
