面向车载网络异常检测的交互式贝叶斯生成模型
Interactive Bayesian Generative Models for Abnormality Detection in Vehicular Networks
AI总结：
该研究提出基于贝叶斯生成模型的V2X网络异常检测方案，通过GDBN学习多模态数据关联，结合IM-MJPF预测网络状态与车辆轨迹，匹配连接图实现高检测概率的车联网链路在线安全监控。
摘要：
本文提出了一种基于贝叶斯生成模型的新型车联网（V2X）网络异常检测方案，用于增强基站（BS）的网络自感知功能。在学习阶段，由车辆集成传感模块采集的多模态数据信号被输入数据驱动的广义动态贝叶斯网络（GDBN）模型。随后在测试阶段，采用交互式改进马尔可夫跳跃粒子滤波器（IM-MJPF），利用耦合多GDBN中嵌入的同化语义预测未来网络状态和车辆轨迹。该方法需学习演化轨迹与网络通信链路之间的统计关联关系。通过将预测的网络连接图与观测到的网络连接图进行匹配，可在线实现车联网（IOV）链路的安全监控，且检测概率较高。
英文摘要：
The following paper proposes a novel Vehicle-to-Everything (V2X) network abnormality detection scheme based on Bayesian generative models for enhanced network self-awareness functionality at the Base station (BS). In the learning phase, multi-modal data signals contrived by the vehicles' integrated and sensing module are imbued into data-driven Generalized Dynamic Bayesian network (GDBN) models. Following that, during the testing phase, an Interactive Modified Markov Jump Particle filter (IM-MJPF) is utilized to forecast forthcoming network states and vehicle trajectories by leveraging the assimilated semantics embedded in the coupled multi-GDBNs. This approach involves learning statistically correlated association between evolving trajectories and network communication links. Security and surveillance of Internet of Vehicles (IOVs) links are performed online with high detection probabilities by matching predicted with observed network connectivity maps (graphs).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
