Area of research
Electrical and Electronic Engineering · Computer Networks and Communications
Research interest
Research focused on Reinforcement learning and Artificial intelligence, with related work in Computer network, Computer security, Markov decision process. Notable publications include 'Enabling Intelligent Connectivity: A Survey of Secure ISAC in 6G Networks', 'When Moving Target Defense Meets Attack Prediction in Digital Twins: A Convolutional and Hierarchical Reinforcement Learning Approach', and 'How to Disturb Network Reconnaissance: A Moving Target Defense Approach Based on Deep Reinforcement Learning'.
Moving Target Defense Meets Artificial-Intelligence-Driven Network: A Comprehensive Survey
Optimizing Resource Allocation for Multi-Modal Semantic Communication in Mobile AIGC Networks: A Diffusion-Based Game Approach
Achieving Network Resilience Through Graph Neural Network-Enabled Deep Reinforcement Learning
Hybrid-Generative Diffusion Models for Attack-Oriented Twin Migration in Vehicular Metaverses
Generative AI-Driven Cross-Layer Covert Communication: Fundamentals, Framework, and Case Study
Enabling Intelligent Connectivity: A Survey of Secure ISAC in 6G Networks
Intelligent Resource Adaptation for Diversified Service Requirements in Industrial IoT
FedASA: A Personalized Federated Learning With Adaptive Model Aggregation for Heterogeneous Mobile Edge Computing
DecFFD: A Personalized Federated Learning Framework for Cross-Location Fault Diagnosis
Securing Federated Diffusion Model With Dynamic Quantization for Generative AI Services in Multiple-Access Artificial Intelligence of Things
EPDB: An Efficient and Privacy-Preserving Electric Charging Scheme in Internet of Robotic Things
An adaptive asynchronous federated learning framework for heterogeneous Internet of things
Towards Secrecy Energy-Efficient RIS Aided UAV Network: A Lyapunov-Guided Reinforcement Learning Approach
Deep Reinforcement Learning-Based Moving Target Defense for Multicast in Software-Defined Satellite Networks
Blockchain and Trusted Hardware-Enabled Data Scheduling for Edge Learning in Wireless IIoT
High-quality Trajectory Generation for Autonomous Driving: A Lightweight Federated Learning-based Diffusion Model
When Moving Target Defense Meets Attack Prediction in Digital Twins: A Convolutional and Hierarchical Reinforcement Learning Approach
How to Disturb Network Reconnaissance: A Moving Target Defense Approach Based on Deep Reinforcement Learning
Towards Attack-Resistant Service Function Chain Migration: A Model-Based Adaptive Proximal Policy Optimization Approach