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An Intelligent Data-Driven Model to Secure Intravehicle Communications Based on Machine Learning

机译：基于机器学习的智能数据驱动模型，以保护基于机器学习的宇宙通信

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摘要

The high relying of electric vehicles on either in-vehicle or between-vehicle communications can cause big issues in the system. This paper is going to mainly address the cyberattack in electric vehicles and propose a secured and reliable intelligent framework to avoid hackers from penetration into the vehicles. The proposed model is constructed based on an improved support vector machine model for anomaly detection based on the controller area network bus protocol. In order to improve the capabilities of the model for fast malicious attack detection and avoidance, a new optimization algorithm based on social spider optimization algorithm is developed, which will reinforce the training process offline. Also, a two-stage modification method is proposed to increase the search ability of the algorithm and avoid premature convergence. Last but not least, the simulation results on the real datasets reveal the high performance, reliability, and security of the proposed model against denial-of-service hacking in the electric vehicles.
机译：在车载内或车辆通信之间的电动车辆的高依赖性可能导致系统中的大问题。本文主要用于电动汽车中的网络攻击，并提出了一种安全可靠的智能框架，以避免黑客从渗透到车辆中。基于基于控制器区域网络总线协议的异常检测的改进的支持向量机模型构建所提出的模型。为了提高模型的快速恶意攻击检测和避免的功能，开发了一种基于社交蜘蛛优化算法的新优化算法，该算法将从下线加强培训过程。此外，提出了一种两级修改方法来提高算法的搜索能力，并避免过早收敛。最后但并非最不重要的是，实时数据集上的仿真结果揭示了拟议模型的高性能，可靠性和安全性，以防止在电动车辆中的拒绝拒绝攻击。

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