College of Computer Science and Information Technology, King Faisal University, P.O. Box 4000, Al-Ahsa 7057, Saudia Arabia.
Deanship of E-Learning and Distance Education, King Faisal University Saudi Arabia, P.O. Box 4000, Al-Ahsa 7057, Saudi Arabia.
Sensors (Basel). 2023 Apr 18;23(8):4086. doi: 10.3390/s23084086.
Connected and autonomous vehicles (CAVs) present exciting opportunities for the improvement of both the mobility of people and the efficiency of transportation systems. The small computers in autonomous vehicles (CAVs) are referred to as electronic control units (ECUs) and are often perceived as being a component of a broader cyber-physical system. Subsystems of ECUs are often networked together via a variety of in-vehicle networks (IVNs) so that data may be exchanged, and the vehicle can operate more efficiently. The purpose of this work is to explore the use of machine learning and deep learning methods in defence against cyber threats to autonomous cars. Our primary emphasis is on identifying erroneous information implanted in the data buses of various automobiles. In order to categorise this type of erroneous data, the gradient boosting method is used, providing a productive illustration of machine learning. To examine the performance of the proposed model, two real datasets, namely the Car-Hacking and UNSE-NB15 datasets, were used. Real automated vehicle network datasets were used in the verification process of the proposed security solution. These datasets included spoofing, flooding and replay attacks, as well as benign packets. The categorical data were transformed into numerical form via pre-processing. Machine learning and deep learning algorithms, namely k-nearest neighbour (KNN) and decision trees, long short-term memory (LSTM), and deep autoencoders, were employed to detect CAN attacks. According to the findings of the experiments, using the decision tree and KNN algorithms as machine learning approaches resulted in accuracy levels of 98.80% and 99%, respectively. On the other hand, the use of LSTM and deep autoencoder algorithms as deep learning approaches resulted in accuracy levels of 96% and 99.98%, respectively. The maximum accuracy was achieved when using the decision tree and deep autoencoder algorithms. Statistical analysis methods were used to analyse the results of the classification algorithms, and the determination coefficient measurement for the deep autoencoder was found to reach a value of R = 95%. The performance of all of the models that were built in this way surpassed that of those already in use, with almost perfect levels of accuracy being achieved. The system developed is able to overcome security issues in IVNs.
联网和自动驾驶汽车 (CAV) 为提高人员的流动性和交通系统的效率带来了令人兴奋的机会。自动驾驶汽车 (CAV) 中的小型计算机称为电子控制单元 (ECU)，通常被视为更广泛的网络物理系统的一个组成部分。ECU 的子系统通常通过各种车载网络 (IVN) 相互连接，以便交换数据，从而使车辆能够更高效地运行。这项工作的目的是探索使用机器学习和深度学习方法来防御自动驾驶汽车的网络威胁。我们的主要重点是识别植入各种汽车数据总线上的错误信息。为了对这种类型的错误数据进行分类，使用梯度提升方法，为机器学习提供了一个富有成效的例证。为了检验所提出模型的性能，使用了两个真实数据集，即 Car-Hacking 和 UNSE-NB15 数据集。在验证所提出的安全解决方案时，使用了真实的自动驾驶汽车网络数据集。这些数据集包括欺骗、泛洪和重播攻击以及良性数据包。通过预处理将分类数据转换为数值形式。使用机器学习和深度学习算法，即 k-最近邻 (KNN) 和决策树、长短时记忆 (LSTM) 和深度自动编码器，来检测 CAN 攻击。根据实验结果，使用决策树和 KNN 算法作为机器学习方法的准确率分别为 98.80%和 99%。另一方面，使用 LSTM 和深度自动编码器算法作为深度学习方法的准确率分别为 96%和 99.98%。使用决策树和深度自动编码器算法时达到了最高的准确率。使用统计分析方法对分类算法的结果进行了分析，发现深度自动编码器的确定系数测量值达到 R=95%。所有构建的模型的性能都超过了现有的模型，准确率几乎达到了完美的水平。所开发的系统能够克服 IVN 中的安全问题。
