As an important component of internet of vehicles (IoV), intelligent
connected vehicles (ICVs) have to communicate with external networks
frequently. In this case, the resource-constrained in-vehicle network (IVN) is
facing a wide variety of complex and changing external cyber-attacks,
especially the masquerade attack with high difficulty of detection while
serious damaging effects that few counter measures can identify successfully.
Moreover, only coarse-grained recognition can be achieved in current mainstream
intrusion detection mechanisms, i.e., whether a whole data flow observation
window contains attack labels rather than fine-grained recognition on every
single data item within this window. In this paper, we propose StatGraph: an
Effective Multi-view Statistical Graph Learning Intrusion Detection to
implement the fine-grained intrusion detection. Specifically, StatGraph
generates two statistical graphs, timing correlation graph (TCG) and coupling
relationship graph (CRG), based on data streams. In given message observation
windows, edge attributes in TCGs represent temporal correlation between
different message IDs, while edge attributes in CRGs denote the neighbour
relationship and contextual similarity. Besides, a lightweight shallow layered
GCN network is trained based graph property of TCGs and CRGs, which can learn
the universal laws of various patterns more effectively and further enhance the
performance of detection. To address the problem of insufficient attack types
in previous intrusion detection, we select two real in-vehicle CAN datasets
that cover four new attacks never investigated before. Experimental result
shows StatGraph improves both detection granularity and detection performance
over state-of-the-art intrusion detection methods.

提出了一种名为 StatGraph 的有效多视图统计图学习入侵检测方法，通过生成统计图并使用轻量级的浅层 GCN 网络来实现细粒度入侵检测，以提高对智能连接车辆中外部网络的复杂和变化的外部网络攻击的识别性和性能。

基于 CAN 消息的多视角统计图学习的车载入侵检测的有效性

Effective In-vehicle Intrusion Detection via Multi-view Statistical  Graph Learning on CAN Messages

Edge intelligence is an emerging paradigm for real-time training and
inference at the wireless edge, thus enabling mission-critical applications.
Accordingly, base stations (BSs) and edge servers (ESs) need to be densely
deployed, leading to huge deployment and operation costs, in particular the
energy costs. In this article, we propose a new framework called
Mobility-Enhanced Edge inTelligence (MEET), which exploits the sensing,
communication, computing, and self-powering capabilities of intelligent
connected vehicles for the smart and green 6G networks. Specifically, the
operators can incorporate infrastructural vehicles as movable BSs or ESs, and
schedule them in a more flexible way to align with the communication and
computation traffic fluctuations. Meanwhile, the remaining compute resources of
opportunistic vehicles are exploited for edge training and inference, where
mobility can further enhance edge intelligence by bringing more compute
resources, communication opportunities, and diverse data. In this way, the
deployment and operation costs are spread over the vastly available vehicles,
so that the edge intelligence is realized cost-effectively and sustainably.
Furthermore, these vehicles can be either powered by renewable energy to reduce
carbon emissions, or charged more flexibly during off-peak hours to cut
electricity bills.

提出了一种基于智能连接车辆的移动增强边缘智能（MEET）框架，利用车辆的感知、通信、计算和自供能力来打造智能、绿色的 6G 网络，从而实现成本效益和可持续性。