A significant increase in the use of electronics control units (ECUs) in modern vehicles has made controller area network (CAN) a de facto standard in the automotive industry. CAN standard has been designed as a reliable and straightforward broadcast-based protocol for providing serial communication between ECUs without considering security aspects like authentication and encryption. Cyber attackers have exploited these vulnerabilities to mount a variety of attacks against CAN-based in-vehicle network. In this work, we proposed a hybrid deep learning-based intrusion detection system (HyDL-IDS) based upon spatial-temporal representation for characterizing in-vehicle network traffic accurately. For this purpose, we use convolutional neural network (CNN) and long short term memory (LSTM) in sequence for extracting spatial and temporal features automatically from in-vehicle network traffic. The proposed HyDL-IDS have been validated using a benchmark car-hacking data set. The reported results demonstrate approximately 100% detection accuracy with a low false alarm rate for different cyber-attacks, including denial-of-service (DoS) attacks, fuzzy attacks and spoofing (Gear and revolutions per minute (RPM)) attacks based on the identified dataset. The HyDL-IDS have significantly improved detection accuracy and false alarm rate for detecting intrusions in-vehicle network compared to other methods, namely Naive Bayes, Decision tree, Multi-layer perceptron, CNN, and LSTM based on spatial-temporal representation of in-vehicle network traffic.

现代车辆中电子控制单元 (ECU) 的使用显着增加，使控制器局域网 (CAN) 成为汽车行业的事实标准。CAN 标准被设计为一种可靠且直接的基于广播的协议，用于在 ECU 之间提供串行通信，而无需考虑身份验证和加密等安全方面。网络攻击者利用这些漏洞对基于 CAN 的车载网络发起了各种攻击。在这项工作中，我们提出了一种基于时空表示的基于混合深度学习的入侵检测系统（HyDL-IDS），用于准确地表征车载网络流量。以此目的，我们依次使用卷积神经网络 (CNN) 和长短期记忆 (LSTM) 从车载网络流量中自动提取空间和时间特征。提议的 HyDL-IDS 已使用基准汽车黑客数据集进行了验证。报告的结果表明，针对不同的网络攻击，包括拒绝服务 (DoS) 攻击、模糊攻击和欺骗（齿轮和每分钟转数 (RPM)）攻击，检测准确率接近 100%，误报率低。识别的数据集。HyDL-IDS与其他基于时空表示的Naive Bayes、Decision tree、Multi-layer perceptron、CNN和LSTM方法相比，显着提高了车载网络检测入侵的检测精度和误报率。车辆网络流量。