3 citations · 5 across the 18 of their papers we have counts for
19 papers
A SoK for SoCs: Reading the TI Leaves on AI for Cyber Threat Intelligence Generation and Sharing
Saastha Vasan, Hadjer Benkraouda, Jizhou Chen +8
Cyber Threat Intelligence (CTI) is essential for defending mission-critical infrastructure, yet the process of transforming raw attack evidence into shareable CTI remains fragmente…
TabQueryBench: A Query-Centric Benchmark for Synthetic Tabular Data
Jialin Zhang, Fenghao Dong, Yajie Zhou +2
Synthetic tabular data support use cases like data sharing, model development under access restrictions, and rapid prototyping of analytical workflows. Modern generative models are…
Agent-Assisted Side-Channel Attacks on Non-Prefix KV Cache in RAG
He Sun, Shinan Liu, Siyuan Ma +3
Modern Large Language Model (LLM) serving engines increasingly rely on Retrieval-Augmented Generation (RAG) and non-prefix Key-Value (KV) cache fusion to accelerate long-context, m…
TraceCodec: A Compiler-Backed Neural Codec for Network Traffic Traces
Junhui Ding, Xinchen Zhang, Xiaohui Xie +1
Critical networking workflows require high-fidelity packet captures (PCAPs) for testing, security analysis, and protocol validation, not just statistical flow-level summaries. Rece…
HillInfer: Efficient Long-Context LLM Inference on the Edge with Hierarchical KV Eviction using SmartSSD
He Sun, Shinan Liu, Li Li +1
Deploying Large Language Models (LLMs) on memory-constrained AI Personal Computers (AIPCs) enables low-latency, privacy-preserving inference, but long-context generation is fundame…
PACC: Protocol-Aware Cross-Layer Compression for Compact Network Traffic Representation
Zhaochen Guo, Tianyufei Zhou, Honghao Wang +2
Network traffic classification underpins network security and management but faces growing challenges from pervasive encryption and evolving protocols. Existing representations inv…
PulseMind: A Multi-Modal Medical Model for Real-World Clinical Diagnosis
Jiao Xu, Junwei Liu, Jiangwei Lao +9
Recent advances in medical multi-modal models focus on specialized image analysis like dermatology, pathology, or radiology. However, they do not fully capture the complexity of re…
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…
SafeCoop: Unravelling Full Stack Safety in Agentic Collaborative Driving
Xiangbo Gao, Tzu-Hsiang Lin, Ruojing Song +6
Collaborative driving systems leverage vehicle-to-everything (V2X) communication across multiple agents to enhance driving safety and efficiency. Traditional V2X systems take raw s…
-GRPO: Unifying the GRPO Frameworks with Learnable Token Preferences
Yining Wang, Jinman Zhao, Chuangxin Zhao +3
Reinforcement Learning with Human Feedback (RLHF) has been the dominant approach for improving the reasoning capabilities of Large Language Models (LLMs). Recently, Reinforcement L…
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…
Algorithmic Data Minimization for Machine Learning over Internet-of-Things Data Streams
Ted Shaowang, Shinan Liu, Jonatas Marques +2
Machine learning can analyze vast amounts of data generated by IoT devices to identify patterns, make predictions, and enable real-time decision-making. By processing sensor data,…
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…
Towards On-Device Evidence Gathering for Intimate Partner Infiltration: A Feasibility Study for Joint Identity-Action Detection
Weisi Yang, Shinan Liu, Feng Xiao +2
Intimate Partner Infiltration (IPI) refers to phone-side privacy infiltration in intimate or close relationships, often enabled by physical access to a person's smartphone and disc…
Feasibility of State Space Models for Network Traffic Generation
Andrew Chu, Xi Jiang, Shinan Liu +4
Many problems in computer networking rely on parsing collections of network traces (e.g., traffic prioritization, intrusion detection). Unfortunately, the availability and utility…
CATO: End-to-End Optimization of ML-Based Traffic Analysis Pipelines
Gerry Wan, Shinan Liu, Francesco Bronzino +2
Machine learning has shown tremendous potential for improving the capabilities of network traffic analysis applications, often outperforming simpler rule-based heuristics. However,…
FlowWise: Stateful Fast-Slow Model Serving for Streaming Traffic Intelligence
Shinan Liu, Ted Shaowang, Gerry Wan +4
Network traffic analysis in cloud and internet service provider (ISP) networks increasingly relies on machine learning (ML) models, as encryption and service consolidation make rul…
NetDiffusion: Network Data Augmentation Through Protocol-Constrained Traffic Generation
Xi Jiang, Shinan Liu, Aaron Gember-Jacobson +4
Datasets of labeled network traces are essential for a multitude of machine learning (ML) tasks in networking, yet their availability is hindered by privacy and maintenance concern…