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AutoML4ETC: Automated Neural Architecture Search for Real-World Encrypted Traffic Classification.

, , , , , , , and . IEEE Trans. Netw. Serv. Manag., 21 (3): 2715-2730 (June 2024)

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A First Look at Generating Website Fingerprinting Attacks via Neural Architecture Search., , , , and . WPES@CCS, page 173-178. ACM, (2023)Editorial for Special Issue on Machine Learning approaches in IoT scenarios., , , and . Comput. Commun., (2022)Host in Danger? Detecting Network Intrusions from Authentication Logs., , , , , and . CNSM, page 1-9. IEEE, (2019)A Report on CNSM 2010., , , and . J. Netw. Syst. Manag., 19 (1): 137-142 (2011)A Critical Study of Few-Shot Learning for Encrypted Traffic Classification., , , , , , , , and . CNSM, page 1-9. IEEE, (2023)Scheduled sampling for robust sensing., and . CNSM, page 224-229. IEEE Computer Society, (2014)QoS and reputation-aware service selection., and . NOMS, page 403-410. IEEE, (2008)Data Drift in DL: Lessons Learned from Encrypted Traffic Classification., , , , , , , and . IFIP Networking, page 1-9. IEEE, (2022)A Machine Learning Approach for RDP-based Lateral Movement Detection., , , , , and . LCN, page 242-245. IEEE, (2019)Meta-ATMoS+: A Meta-Reinforcement Learning Framework for Threat Mitigation in Software-Defined Networks., , , and . LCN, page 1-9. IEEE, (2023)