Incremental encrypted traffic classification via contrastive prototype networks
Author:
Funder
National Key Research and Development Program of China
Publisher
Elsevier BV
Reference58 articles.
1. Blake Anderson, David McGrew, Machine learning for encrypted malware traffic classification: Accounting for noisy labels and non-stationarity, in: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017, pp. 1723–1732.
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1. A novel approach for application classification with encrypted traffic using BERT and packet headers;Computer Networks;2024-12
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