Applied College in Abqaiq, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia.
College of Computer Science and Information Technology, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia.
Sensors (Basel). 2022 Jan 4;22(1):360. doi: 10.3390/s22010360.
Rapid technological development has changed drastically the automotive industry. Network communication has improved, helping the vehicles transition from completely machine- to software-controlled technologies. The autonomous vehicle network is controlled by the controller area network (CAN) bus protocol. Nevertheless, the autonomous vehicle network still has issues and weaknesses concerning cybersecurity due to the complexity of data and traffic behaviors that benefit the unauthorized intrusion to a CAN bus and several types of attacks. Therefore, developing systems to rapidly detect message attacks in CAN is one of the biggest challenges. This study presents a high-performance system with an artificial intelligence approach that protects the vehicle network from cyber threats. The system secures the autonomous vehicle from intrusions by using deep learning approaches. The proposed security system was verified by using a real automatic vehicle network dataset, including spoofing, flood, replaying attacks, and benign packets. Preprocessing was applied to convert the categorical data into numerical. This dataset was processed by using the convolution neural network (CNN) and a hybrid network combining CNN and long short-term memory (CNN-LSTM) models to identify attack messages. The results revealed that the model achieved high performance, as evaluated by the metrics of precision, recall, F1 score, and accuracy. The proposed system achieved high accuracy (97.30%). Along with the empirical demonstration, the proposed system enhanced the detection and classification accuracy compared with the existing systems and was proven to have superior performance for real-time CAN bus security.
快速的技术发展彻底改变了汽车行业。网络通信得到了改善，帮助车辆从完全由机器控制的技术过渡到软件控制的技术。自动驾驶汽车网络由控制器局域网（CAN）总线协议控制。然而，由于数据和流量行为的复杂性，自动驾驶汽车网络在网络安全方面仍然存在问题和弱点，这有利于未经授权的入侵 CAN 总线和多种类型的攻击。因此，开发能够快速检测 CAN 中消息攻击的系统是最大的挑战之一。本研究提出了一种具有人工智能方法的高性能系统，该系统可以保护车辆网络免受网络威胁。该系统通过使用深度学习方法来保护自动驾驶汽车免受入侵。所提出的安全系统使用真实的自动驾驶网络数据集进行了验证，包括欺骗、洪水、重放攻击和良性数据包。应用预处理将分类数据转换为数值。该数据集使用卷积神经网络（CNN）和结合 CNN 和长短时记忆（CNN-LSTM）模型的混合网络进行处理，以识别攻击消息。结果表明，该模型通过精度、召回率、F1 得分和准确性等指标评估具有较高的性能。所提出的系统达到了 97.30%的高准确率。除了实证演示外，与现有系统相比，所提出的系统提高了检测和分类的准确性，并被证明在实时 CAN 总线安全方面具有优越的性能。
