The Internet of Vehicles (IoV) is advancing modern transportation by improving safety, efficiency, and intelligence. However, the reliance on the Controller Area Network (CAN) introduces critical security risks, as CAN-based communication is highly vulnerable to cyberattacks. Addressing this challenge, we propose DAIRE (Detecting Attacks in IoV in REal-time), a lightweight machine learning framework designed for real-time detection and classification of CAN attacks. DAIRE is built on a lightweight artificial neural network (ANN) where each layer contains Ni = i x c neurons, with Ni representing the number of neurons in the ith layer and c corresponding to the total number of attack classes. Other hyperparameters are determined empirically to ensure real-time operation. To support the detection and classification of various IoV attacks, such as Denial-of-Service, Fuzzy, and Spoofing, DAIRE employs the sparse categorical cross-entropy loss function and root mean square propagation for loss minimization. In contrast to more resource-intensive architectures, DAIRE leverages a lightweight ANN to reduce computational demands while still delivering strong performance. Experimental results on the CICIoV2024 and Car-Hacking datasets demonstrate DAIRE's effectiveness, achieving an average detection rate of 99.88%, a false positive rate of 0.02%, and an overall accuracy of 99.96%. Furthermore, DAIRE significantly outperforms state-of-the-art approaches in inference speed, with a classification time of just 0.03 ms per sample. These results highlight DAIRE's effectiveness in detecting IoV cyberattacks and its practical suitability for real-time deployment in vehicular systems, underscoring its vital role in strengthening automotive cybersecurity.

翻译：车联网正通过提升安全性、效率与智能化水平推动现代交通发展。然而，对控制器区域网络的依赖引入了关键性安全风险，因为基于CAN的通信极易受到网络攻击。针对这一挑战，我们提出DAIRE——一个专为实时检测与分类CAN攻击设计的轻量级机器学习框架。DAIRE基于轻量级人工神经网络构建，其每层包含Ni = i × c个神经元，其中Ni表示第i层神经元数量，c对应攻击类别总数。其他超参数通过经验确定以确保实时运行。为支持各类车联网攻击（如拒绝服务攻击、模糊攻击、欺骗攻击）的检测与分类，DAIRE采用稀疏分类交叉熵损失函数及均方根传播算法进行损失最小化。区别于资源密集型架构，DAIRE通过轻量级ANN降低计算需求的同时保持强劲性能。基于CICIoV2024与Car-Hacking数据集的实验结果表明，DAIRE实现了99.88%的平均检测率、0.02%的误报率及99.96%的总体准确率。此外，DAIRE在推理速度上显著超越现有最优方法，每个样本分类时间仅需0.03毫秒。这些结果印证了DAIRE在检测车联网网络攻击中的有效性及其在车载系统中实时部署的实践可行性，凸显了其在强化汽车网络安全中的关键作用。