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A Comparison of Machine and Deep Learning Models for Detection and Classification of Android Malware Traffic., , , , , and . ISCC, page 1-6. IEEE, (2022)Network anomaly detection methods in IoT environments via deep learning: A Fair comparison of performance and robustness., , , , , and . Comput. Secur., (May 2023)Computational intelligence intrusion detection techniques in mobile cloud computing environments: Review, taxonomy, and open research issues., , , , , and . J. Inf. Secur. Appl., (2020)On the Integration of Blockchain and SDN: Overview, Applications, and Future Perspectives., , , , , , , and . CoRR, (2022)Anonymity Services Tor, I2P, JonDonym: Classifying in the Dark (Web)., , , and . IEEE Trans. Dependable Secur. Comput., 17 (3): 662-675 (2020)Mobile Encrypted Traffic Classification Using Deep Learning: Experimental Evaluation, Lessons Learned, and Challenges., , , and . IEEE Trans. Network and Service Management, 16 (2): 445-458 (2019)Human behavior sensing: challenges and approaches., , , and . J. Ambient Intell. Humaniz. Comput., 11 (12): 6043-6058 (2020)Fine-Grained Traffic Prediction of Communication-and-Collaboration Apps Via Deep-Learning: A First Look at Explainability., , , , , and . ICC, page 1609-1615. IEEE, (2023)BlockSD-5GNet: Enhancing security of 5G network through blockchain-SDN with ML-based bandwidth prediction., , , , , , , , and . Trans. Emerg. Telecommun. Technol., (April 2024)Unveiling MIMETIC: Interpreting Deep Learning Traffic Classifiers via XAI Techniques., , , , , and . CSR, page 455-460. IEEE, (2021)