Machine Learning-Based Intrusion Detection System for Controller Area Networks
Machine Learning-Based Intrusion Detection System for Controller Area Networks
复制标题
基于机器学习的控制器局域网入侵检测系统
DOI：
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复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
K. El
中科院分区：
文献类型：
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作者：
Omar Minawi;Jason Whelan;Abdulaziz Almehmadi;K. El
汽车行业继续以指数级的速度创新，为消费者提供更安全、更高效的体验。自动驾驶汽车和车辆到一切的技术处于定义未来交通运输的前沿。使车辆能够连接到各种服务使关键的车载网络(如控制器区域网络(CAN))面临潜在的对手攻击。在其标准形式中，CAN总线存在多个漏洞，如带宽有限和缺乏身份验证。攻击可以通过物理和无线媒介发起，利用诊断界面、蓝牙和信息娱乐系统来危害车辆内数据通信的机密性、完整性和可用性。本文针对关键车载网络的安全问题，提出了一种基于机器学习的全面、全面的车载网络入侵检测系统。建议的系统是模块化的、可扩展的，并且可以适应不断变化的网络车辆攻击的威胁格局。在一个不可见的测试数据集上，我们的系统在防御拒绝服务攻击和多次冒充注入攻击方面达到了100%的准确率，而对于模糊注入攻击的准确率达到了95.67%。
The automotive industry continues to innovate at an exponential rate to provide a safer and more efficient experience for consumers. Autonomous vehicles and Vehicle-to-Everything technologies are at the forefront of defining the future of transportation. Enabling vehicles to connect to various services has exposed critical in-vehicle networks such as the Controller Area Network (CAN) to potential exploitation by adversaries. In its standard form, the CAN bus suffers from multiple vulnerabilities such as limited bandwidth and lack of authentication. Attacks can be initiated through physical and wireless mediums, exploiting diagnostic interfaces, Bluetooth and infotainment systems to compromise the confidentiality, integrity and availability of data communication within vehicles. In this paper, a holistic, comprehensive, Machine Learning-Based intrusion detection system for the CAN bus is proposed to secure the critical in-vehicle network. The proposed system is modular, scalable and can be adapted to the ever-changing threat landscape of cyber vehicle attacks. On an unseen testing dataset, our system achieved 100% accuracy in protecting against denial of service and multiple impersonation injection attacks, as well as 95.67% accuracy of fuzzy injection attacks.
基于跨车辆驾驶模式的恒定 CAN 消息频率的汽车入侵检测
DOI：
10.1145/3309171.3309179
发表时间：
2019
期刊：
Proceedings of the ACM Workshop on Automotive Cybersecurity
影响因子：
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作者：
Young, Clinton;Olufowobi, Habeeb;Bloom, Gedare;Zambreno, Joseph
通讯作者：
Zambreno, Joseph