# Hybrid-Generative Diffusion Models for Attack-Oriented Twin Migration in Vehicular Metaverses arXiv: https://arxiv.org/abs/2407.11036 ยท HTML: https://mlsanity.com/paper/2407.11036/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models. - Builds on 25 of its 59 references that are in the map. - First posted on arXiv 5 Jul 2024. ## Placed in - Diffusion models / [Diffusion and flow policies for robotics](https://mlsanity.com/diffusion/diffusion-and-flow-policies-for-robotics/) ## Builds on (most cited first, 25 of 25) - [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020) - [All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agenda](https://mlsanity.com/paper/2110.05352/) (2021) - [MDM: Molecular Diffusion Model for 3D Molecule Generation](https://mlsanity.com/paper/2209.05710/) (2022) - [Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization](https://mlsanity.com/paper/2308.05384/) (2023) - [A Survey on Metaverse: Fundamentals, Security, and Privacy](https://mlsanity.com/paper/2203.02662/) (2022) - [Efficient Diffusion Models for Vision: A Survey](https://mlsanity.com/paper/2210.09292/) (2022) - [Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Services](https://mlsanity.com/paper/2303.13052/) (2023) - [AI-Generated Incentive Mechanism and Full-Duplex Semantic Communications for Information Sharing](https://mlsanity.com/paper/2303.01896/) (2023) - [Deep Generative Model and Its Applications in Efficient Wireless Network Management: A Tutorial and Case Study](https://mlsanity.com/paper/2303.17114/) (2023) - [From Generative AI to Generative Internet of Things: Fundamentals, Framework, and Outlooks](https://mlsanity.com/paper/2310.18382/) (2023) - [Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshness](https://mlsanity.com/paper/2307.15975/) (2023) - [Multi-Agent Deep Reinforcement Learning for Dynamic Avatar Migration in AIoT-enabled Vehicular Metaverses with Trajectory Prediction](https://mlsanity.com/paper/2306.14683/) (2023) - [Privacy Attacks and Defenses for Digital Twin Migrations in Vehicular Metaverses](https://mlsanity.com/paper/2309.00477/) (2023) - [Diffusion Model-based Incentive Mechanism with Prospect Theory for Edge AIGC Services in 6G IoT](https://mlsanity.com/paper/2407.10979/) (2024) - [Performance Comparison of Intrusion Detection Systems and Application of Machine Learning to Snort System](https://mlsanity.com/paper/1710.04843/) (2017) - [Blockchain-assisted Twin Migration for Vehicular Metaverses: A Game Theory Approach](https://mlsanity.com/paper/2309.02173/) (2023) - [Task Freshness-aware Incentive Mechanism for Vehicle Twin Migration in Vehicular Metaverses](https://mlsanity.com/paper/2309.04703/) (2023) - [Resource-efficient Generative Mobile Edge Networks in 6G Era: Fundamentals, Framework and Case Study](https://mlsanity.com/paper/2312.12063/) (2023) - [Bi-directional Digital Twin and Edge Computing in the Metaverse](https://mlsanity.com/paper/2211.08700/) (2022) - [Resource Allocation for Augmented Reality Empowered Vehicular Edge Metaverse](https://mlsanity.com/paper/2212.01325/) (2022) - [When Metaverses Meet Vehicle Road Cooperation: Multi-Agent DRL-Based Stackelberg Game for Vehicular Twins Migration](https://mlsanity.com/paper/2312.17081/) (2023) - [Blockchain-based Pseudonym Management for Vehicle Twin Migrations in Vehicular Edge Metaverse](https://mlsanity.com/paper/2403.15285/) (2024) - [Joint Compute-Caching-Communication Control for Online Data-Intensive Service Delivery](https://mlsanity.com/paper/2205.01944/) (2022) - [Vetaverse: A Survey on the Intersection of Metaverse, Vehicles, and Transportation Systems](https://mlsanity.com/paper/2210.15109/) (2022) - [A Subjective-Logic-based Reliability Estimation Mechanism for Cooperative Information with Application to IV's Safety](https://mlsanity.com/paper/1903.01556/) (2019) ## Built on by (most cited first, 7 of 7) - [Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Art](https://mlsanity.com/paper/2507.16733/) (2025) - [Diffusion Models for Future Networks and Communications: A Comprehensive Survey](https://mlsanity.com/paper/2508.01586/) (2025) - [Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models](https://mlsanity.com/paper/2502.07211/) (2025) - [Generative Diffusion Receivers: Achieving Pilot-Efficient MIMO-OFDM Communications](https://mlsanity.com/paper/2506.18419/) (2025) - [Confidence-Regulated Generative Diffusion Models for Reliable AI Agent Migration in Vehicular Metaverses](https://mlsanity.com/paper/2505.12710/) (2025) - [Cross-reality location privacy protection in 6G-enabled vehicular metaverses: an LLM-enhanced hybrid generative diffusion model-based approach](https://mlsanity.com/paper/2601.12311/) (2026) - [From Denoising to Decision Making: A Survey on Diffusion Model-Enabled Deep Reinforcement Learning for Wireless Networks](https://mlsanity.com/paper/2605.25531/) (2026) ## Abstract (arXiv) The vehicular metaverse is envisioned as a blended immersive domain that promises to bring revolutionary changes to the automotive industry. As a core component of vehicular metaverses, Vehicle Twins (VTs) are digital twins that cover the entire life cycle of vehicles, providing immersive virtual services for Vehicular Metaverse Users (VMUs). Vehicles with limited resources offload the computationally intensive tasks of constructing and updating VTs to edge servers and migrate VTs between these servers, ensuring seamless and immersive experiences for VMUs. However, the high mobility of vehicles, uneven deployment of edge servers, and potential security threats pose challenges to achieving efficient and reliable VT migrations. To address these issues, we propose a secure and reliable VT migration framework in vehicular metaverses. Specifically, we design a two-layer trust evaluation model to comprehensively evaluate the reputation value of edge servers in the network communication and interaction layers. Then, we model the VT migration problem as a partially observable Markov decision process and design a hybrid-Generative Diffusion Model (GDM) algorithm based on deep reinforcement learning to generate optimal migration decisions by taking hybrid actions (i.e., continuous actions and discrete actions). Numerical results demonstrate that the hybrid-GDM algorithm outperforms the baseline algorithms, showing strong adaptability in various settings and highlighting the potential of the hybrid-GDM algorithm for addressing various optimization issues in vehicular metaverses.