Research group at Budapest University of Technology and Economics, focused on applied machine learning for network traffic analysis and security.
- Encrypted traffic analysis — VPN detection, QUIC classification, malware detection over encrypted channels
- Federated learning — privacy-preserving traffic classification under non-IID and temporally volatile conditions
- Anomaly detection — ML-based network security, dataset integrity, service degradation detection
- Network measurement — flow-level compression, programmable data planes, traffic classification
| Repository | Description |
|---|---|
| IFLforTFC | Incremental federated learning for traffic flow classification |
| FL-QUIC-TC | Federated QUIC traffic classification |
| privacy-preserving-federated-learning | Framework for experimenting with privacy-preserving mechanisms in FL |
| Repository | Description |
|---|---|
| CyberML-DataQuality | Evaluating ML-based anomaly detection across datasets of varied integrity |
| CyberML-CompleteVsFirstN | Early-stage anomaly detection: complete vs. partial flows |
| ServDeg-Dataset | Latency-induced service degradation: methodology and dataset |
| ServDeg-Inter | Inter-flow service degradation detection |
Group lead: Adrián Pekár — Google Scholar