基于AI的车联网安全算法-硬件分离

M. Aman, B. Sikdar
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引用次数: 1

摘要

汽车互联网正在成为一个令人兴奋的应用,它可以提高安全性,并以主动道路标志、现收现付保险、电子收费和车队管理的形式提供更好的服务。网联汽车面临着网络威胁形式的新攻击载体,随着网络攻击的增加,自动驾驶汽车的成功取决于其安全性。现有的车联网安全技术是基于一个不切实际的假设,即所有车辆都配备了相同的硬件(至少在计算能力方面)。然而,各种汽车制造商使用的硬件平台是高度异构的。因此,为iov设计的安全协议应该能够检测底层平台的计算能力,并相应地调整安全原语。为了解决这一问题,本文提出了一种车联网安全的算法-硬件分离技术。该技术使用迭代例程和相应的执行时间,使用基于人工智能的推理引擎来检测硬件平台的计算能力。在三种不同的常用微控制器上的实验结果表明,该方法可以有效地检测硬件平台的类型,准确率高达100%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AI Based Algorithm-Hardware Separation for IoV Security
The Internet of vehicles is emerging as an exciting application to improve safety and providing better services in the form of active road signs, pay-as-you-go insurance, electronic toll, and fleet management. Internet connected vehicles are exposed to new attack vectors in the form of cyber threats and with the increasing trend of cyber attacks, the success of autonomous vehicles depends on their security. Existing techniques for IoV security are based on the un-realistic assumption that all the vehicles are equipped with the same hardware (at least in terms of computational capabilities). However, the hardware platforms used by various vehicle manufacturers are highly heterogeneous. Therefore, a security protocol designed for IoVs should be able to detect the computational capabilities of the underlying platform and adjust the security primitives accordingly. To solve this issue, this paper presents a technique for algorithm-hardware separation for IoV security. The proposed technique uses an iterative routine and the corresponding execution time to detect the computational capabilities of a hardware platform using an artificial intelligence based inference engine. The results on three different commonly used micro-controllers show that the proposed technique can effectively detect the type of hardware platform with up to 100% accuracy.
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