Self-Supervised Learning Meets Custom Autoencoder Classifier: A Semi-Supervised Approach for Encrypted Traffic Anomaly Detection

The widespread adoption of encryption in computer networks has made detecting malicious traffic, especially at network perimeters, increasingly challenging. As packet contents are concealed, traditional monitoring techniques such as Deep Packet Inspection (DPI) become ineffective. Consequently, rese...

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Bibliografische Detailangaben

Hauptverfasser:	A. Ramzi Bahlali, Abdelmalik Bachir, Abdeldjalil Labed
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	IEEE 2025-01-01
Schriftenreihe:	IEEE Access
Schlagworte:	Autoencoder (AE) anomaly detection (AD) deep learning (DL) intrusion detection self-supervised learning (SSL) semi-supervised Learning
Online-Zugang:	https://ieeexplore.ieee.org/document/11113262/
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