FastDet: Detecting Encrypted Malicious Traffic Faster via Early Exit
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-0834-5_18
Reference22 articles.
1. Torroledo, I., Camacho, L.D., Bahnsen, A.C.: Hunting malicious tls certificates with deep neural networks. In: Proceedings of the 11th ACM Workshop on Artificial Intelligence and Security, pp. 64–73 (2018)
2. Anderson, B., McGrew, D., Acm: Machine learning for encrypted malware traffic classification: Accounting for noisy labels and non-stationarity. In: Kdd’17: Proceedings of the 23rd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 1723–1732 (2017)
3. Tong, X., Tan, X., Chen, L., Yang, J., Zheng, Q.: Bfsn: a novel method of encrypted traffic classification based on bidirectional flow sequence network. In: 2020 3rd International Conference on Hot Information-Centric Networking (HotICN), pp. 160–165. IEEE (2020)
4. Prasse, P., Knaebel, R., Machlica, L., Pevny, T., Scheffer, T.: Joint detection of malicious domains and infected clients. Mach. Learn. 108(8–9), 1353–1368 (2019)
5. Zeng, Y., Gu, H., Wei, W., Guo, Y.: $$ deep-full-range $$: a deep learning based network encrypted traffic classification and intrusion detection framework. IEEE Access 7, 45182–45190 (2019)
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