Deploying Connected and Automated Vehicles (CAVs) on top of 5G and Beyond networks (5GB) makes them vulnerable to increasing vectors of security and privacy attacks. In this context, a wide range of advanced machine/deep learning based solutions have been designed to accurately detect security attacks. Specifically, supervised learning techniques have been widely applied to train attack detection models. However, the main limitation of such solutions is their inability to detect attacks different from those seen during the training phase, or new attacks, also called zero-day attacks. Moreover, training the detection model requires significant data collection and labeling, which increases the communication overhead, and raises privacy concerns. To address the aforementioned limits, we propose in this paper a novel detection mechanism that leverages the ability of the deep auto-encoder method to detect attacks relying only on the benign network traffic pattern. Using federated learning, the proposed intrusion detection system can be trained with large and diverse benign network traffic, while preserving the CAVs privacy, and minimizing the communication overhead. The in-depth experiment on a recent network traffic dataset shows that the proposed system achieved a high detection rate while minimizing the false positive rate, and the detection delay.

翻译：在5G及未来网络（5GB）上部署网联自动驾驶汽车（CAVs）使其面临日益增长的安全与隐私攻击向量威胁。在此背景下，已设计出多种基于先进机器学习/深度学习的解决方案以精确检测安全攻击。具体而言，监督学习技术已被广泛应用于训练攻击检测模型。然而，此类解决方案的主要局限在于无法检测与训练阶段所见攻击不同的新型攻击（亦称为零日攻击）。此外，训练检测模型需要大规模数据收集与标注，这不仅增加了通信开销，也引发了隐私担忧。为克服上述局限，本文提出一种新型检测机制，该机制利用深度自编码器方法仅依赖良性网络流量模式检测攻击的能力。通过采用联邦学习，所提出的入侵检测系统可利用大规模多样化的良性网络流量进行训练，同时保护CAVs隐私并最小化通信开销。基于最新网络流量数据集的深入实验表明，所提系统在实现高检测率的同时，有效降低了误报率与检测延迟。