5 papers
Let AI Agents Translate Networks, Not Reason About Them
Hongyu Hè, Maria Apostolaki
A formal model enables verifying reachability, localizing an outage, or anticipating the blast radius of a change. Yet, virtually no production network has one, since writing a mod…
Invariant Discovery for Networked Systems
Hongyu Hè, Alexander Krentsel, Sylvia Ratnasamy +1
Invariants, the relations expected to hold among measured signals of a network, underpin applications from verification to traffic generation, telemetry imputation, and input valid…
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…