6 papers · 1 filter
Tutorial on Flow-Based Network Traffic Classification Using Machine Learning
Adrian Pekar, Richard Plny, Karel Hynek
Modern networks carry increasingly diverse and encrypted traffic types that demand classification techniques beyond traditional port-based and payload-based methods. This tutorial…
On the Feasibility of Inter-Flow Service Degradation Detection
Balint Bicski, Adrian Pekar
Hardware acceleration in modern networks creates monitoring blind spots by offloading flows to a non-observable state, hindering real-time service degradation (SD) detection. To ad…
Taming Volatility: Stable and Private QUIC Classification with Federated Learning
Richard Jozsa, Karel Hynek, Adrian Pekar
Federated Learning (FL) is a promising approach for privacy-preserving network traffic analysis, but its practical deployment is challenged by the non-IID nature of real-world data…
Binary VPN Traffic Detection Using Wavelet Features and Machine Learning
Yasameen Sajid Razooqi, Adrian Pekar
Encrypted traffic classification faces growing challenges as encryption renders traditional deep packet inspection ineffective. This study addresses binary VPN detection, distingui…
AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression with Minimal Impact on Traffic Classification
Adrian Pekar
Network monitoring generates massive volumes of IP flow records, posing significant challenges for storage and analysis. This paper presents a novel deep learning-based approach to…
Early Detection of Network Service Degradation: An Intra-Flow Approach
Balint Bicski, Adrian Pekar
This research presents a novel method for predicting service degradation (SD) in computer networks by leveraging early flow features. Our approach focuses on the observable (O) seg…