3 papers
cs.NI2025
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…
cs.LG2025
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…
cs.LG2024
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…