Vehicular communication networks are rapidly emerging as vehicles become
smarter. However, these networks are increasingly susceptible to various
attacks. The situation is exacerbated by the rise in automated vehicles
complicates, emphasizing the need for security and authentication measures to
ensure safe and effective traffic management. In this paper, we propose a novel
hybrid physical layer security (PLS)-machine learning (ML) authentication
scheme by exploiting the position of the transmitter vehicle as a device
fingerprint. We use a time-of-arrival (ToA) based localization mechanism where
the ToA is estimated at roadside units (RSUs), and the coordinates of the
transmitter vehicle are extracted at the base station (BS).Furthermore, to
track the mobility of the moving legitimate vehicle, we use ML model trained on
several system parameters. We try two ML models for this purpose, i.e., support
vector regression and decision tree. To evaluate our scheme, we conduct binary
hypothesis testing on the estimated positions with the help of the ground
truths provided by the ML model, which classifies the transmitter node as
legitimate or malicious. Moreover, we consider the probability of false alarm
and the probability of missed detection as performance metrics resulting from
the binary hypothesis testing, and mean absolute error (MAE), mean square error
(MSE), and coefficient of determination $\text{R}^2$ to further evaluate the ML
models. We also compare our scheme with a baseline scheme that exploits the
angle of arrival at RSUs for authentication. We observe that our proposed
position-based mechanism outperforms the baseline scheme significantly in terms
of missed detections.

本文提出了一种基于位置和机器学习的身份验证方案，通过利用发射车辆的位置作为设备指纹，以实现车载通信网络的安全和有效的交通管理。采用了基于到达时间的定位机制和机器学习模型，通过构建支持向量回归和决策树模型评估了方案的性能优势，并在与基准方案的比较中显示出更好的性能表现。

V2I 通信网络的混合 PLS-ML 认证方案

Hybrid PLS-ML Authentication Scheme for V2I Communication Networks

In this letter, an age of information (AoI)-aware transmission power and
resource block (RB) allocation technique for vehicular communication networks
is proposed. Due to the highly dynamic nature of vehicular networks, gaining a
prior knowledge about the network dynamics, i.e., wireless channels and
interference, in order to allocate resources, is challenging. Therefore, to
effectively allocate power and RBs, the proposed approach allows the network to
actively learn its dynamics by balancing a tradeoff between minimizing the
probability that the vehicles' AoI exceeds a predefined threshold and
maximizing the knowledge about the network dynamics. In this regard, using a
Gaussian process regression (GPR) approach, an online decentralized strategy is
proposed to actively learn the network dynamics, estimate the vehicles' future
AoI, and proactively allocate resources. Simulation results show a significant
improvement in terms of AoI violation probability, compared to several
baselines, with a reduction of at least 50%.

本文提出了一种面向车联网的信息时代感知传输功率和资源块分配技术，利用高斯过程回归方法实现在线无中央控制机制的网络动态学习，实时预测未来 AoI，最小化 AoI 超过阈值的概率，并取得了 50% 以上的效果提升。