A controller area network (CAN) is a communication protocol for in-vehicle networks. Communication between electronic control units (ECUs) is facilitated by the CAN bus. This communication protocol provides no authentication or encryption to prevent the consequences of cyberattacks. As a security measure for this protocol, we have proposed an intrusion detection system (IDS) using a convolutional neural network (CNN). The CNN is trained on recurrence images generated from the encoded labels of CAN frame arbitration IDs, thus Rec-CNN. Using recurrence plots helps us capture the temporal dependency in the sequence of arbitration IDs unlike the state-of-art method, which does not capture this information. We have tested the proposed method on a publicly available dataset with denial of service (DoS), fuzzy, spoofing-gear, and spoofing-RPM attacks, resulting in an accuracy of 0.999. Furthermore, we have experimented with the method on our target vehicle. The proposed method can classify our simulated attacks with an accuracy of 0.999 in an attack frequency of 10 ms.

控制器局域网 (CAN) 是用于车载网络的通信协议。CAN 总线促进了电子控制单元 (ECU) 之间的通信。此通信协议不提供身份验证或加密来防止网络攻击的后果。作为该协议的安全措施，我们提出了一种使用卷积神经网络 (CNN) 的入侵检测系统 (IDS)。CNN 在从 CAN 帧仲裁 ID 的编码标签生成的递归图像上进行训练，因此是 Rec-CNN。使用递归图可以帮助我们捕捉仲裁 ID 序列中的时间依赖性，这与最先进的方法不同，后者不捕捉这些信息。我们已经在具有拒绝服务 (DoS)、模糊、spoofing-gear 和 spoofing-RPM 攻击的公开可用数据集上测试了所提出的方法，得到 0.999 的准确度。此外，我们已经在目标车辆上对该方法进行了试验。所提出的方法可以在 10 ms 的攻击频率下以 0.999 的准确度对我们的模拟攻击进行分类。