16 citations · 18 across the 10 of their papers we have counts for
10 papers
Feature Suppression and Differential Privacy for Residential Traffic Classification: A Two-Home Federated Study
Márton Pál Lipcsey-Magyar, Adrian Pekar
Residential traffic classification supports service management, but learning across homes must account for heterogeneous traffic and privacy constraints. Privacy-aware training may…
Matched-View Cross-Domain Evaluation of WireGuard VPN Traffic Classification Using Early-Flow Fingerprints
Yasameen Sajid Razooqi, Adrian Pekar
Classifying VPN-encrypted traffic by application category typically relies on datasets that collect non-VPN and VPN traffic in separate sessions, conflating encapsulation effects w…
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…
Early-Stage Anomaly Detection: A Study of Model Performance on Complete vs. Partial Flows
Adrian Pekar, Richard Jozsa
This study investigates the efficacy of machine learning models in network security threat detection through the critical lens of partial versus complete flow information, addressi…
Evaluating ML-Based Anomaly Detection Across Datasets of Varied Integrity: A Case Study
Adrian Pekar, Richard Jozsa
Cybersecurity remains a critical challenge in the digital age, with network traffic flow anomaly detection being a key pivotal instrument in the fight against cyber threats. In thi…