arXiv Machine Learning August 18, 2026 By Wumei Du, Jiarong Wen, Kaiyu Zhang, Zi Yang, Yiqin Lv, Longfei Zhang, Dong Liang, Zheng Xie

PERO: Efficient Robust Post-Training Foundation Models for Encrypted Traffic Classification

Read the original on arXiv Machine Learning →

arXiv:2608. 15504v1 Announce Type: new Abstract: Encrypted traffic classification is vital for network security, yet real-world deployments are inherently sensitive to rare but high-loss errors such as misclassification of malicious traffic.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat Detection

Treat Traffic Like Trees: A Semantic-Preserving Hierarchical Graph-Based Expert Framework for Encrypted Traffic Analysis

PLAA: Packet-level Adversarial Attacks in Network Traffic Detection

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