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What is Detecting Encrypted Malicious Traffic with MIL about?

This paper presents a novel framework for detecting encrypted malicious traffic using multi-instance learning (MIL) that aggregates network flows into bags and employs attention mechanisms to enhance detection accuracy. The approach processes encrypted traffic through burst segmentation, CNN-based feature extraction, and attention pooling, effectively isolating malicious activities embedded in benign flows. Experimental results demonstrate significant improvements in detection performance compared to tradit

Author

samib8104

Language

EN