2 citations · 2 across the 2 of their papers we have counts for
5 papers
Quantifying the Privacy Implications of High-Fidelity Synthetic Network Traffic
Van Tran, Shinan Liu, Tian Li +1
To address the scarcity and privacy concerns of network traffic data, various generative models have been developed to produce synthetic traffic. However, synthetic traffic is not…
WiFinger: Fingerprinting Noisy IoT Event Traffic Using Packet-level Sequence Matching
Ronghua Li, Shinan Liu, Haibo Hu +2
IoT environments such as smart homes are susceptible to privacy inference attacks, where attackers can analyze patterns of encrypted network traffic to infer the state of devices a…
NetSSM: Multi-Flow and State-Aware Network Trace Generation using State Space Models
Andrew Chu, Xi Jiang, Shinan Liu +4
Access to raw network traffic data is essential for many computer networking tasks, from traffic modeling to performance evaluation. Unfortunately, this data is scarce due to high…
Generative Active Adaptation for Drifting and Imbalanced Network Intrusion Detection
Ragini Gupta, Shinan Liu, Ruixiao Zhang +7
Machine learning has shown promise in network intrusion detection systems, yet its performance often degrades due to concept drift and imbalanced data. These challenges are compoun…
CAIP: Detecting Router Misconfigurations with Context-Aware Iterative Prompting of LLMs
Xi Jiang, Aaron Gember-Jacobson, Nick Feamster
Model checkers and consistency checkers detect critical errors in router configurations, but these tools require significant manual effort to develop and maintain. LLM-based Q&A mo…