5 papers
The Frontier LLM Trap in Network Automation
Minhao Jin, Sean Wang, Aarti Gupta +1
Large LLMs are powerful tools for network automation, but they are expensive, slow to serve, hard to audit, poorly tailored to individual networks, and create long-term dependencie…
Cross-Flow Correlations Survive Synthesis: Measuring Source-Level Privacy Leakage in Synthetic Network Traces
Minhao Jin, Hongyu Hè, Maria Apostolaki
Synthetic network data generators (SynNetGens) are increasingly used to share realistic traffic traces without exposing sensitive raw data. While substantial effort has gone into i…
Worst-Case Discovery and Runtime Protection for RL-Based Network Controllers
Hongyu Hè, Minhao Jin, Maria Apostolaki
RL-based controllers achieve strong average-case performance in networking tasks such as congestion control and adaptive bitrate streaming. Yet their performance can degrade severe…
Making Logic a First-Class Citizen in Generative ML for Networking
Hongyu Hè, Minhao Jin, Maria Apostolaki
Generative ML models are increasingly popular in networking for tasks such as telemetry imputation, prediction, and synthetic trace generation. Despite their capabilities, they suf…
Robustifying ML-powered Network Classifiers with PANTS
Minhao Jin, Maria Apostolaki
Multiple network management tasks, from resource allocation to intrusion detection, rely on some form of ML-based network traffic classification (MNC). Despite their potential, MNC…