Sensor-equipped connected vehicle networks (SECVNs) have the potential to enable substantially safer driving by improved object tracking, which is an important basic building block in SECVNs. Unfortunately, cyber-attacks on SECVNs pose a very serious threat which could lead to unacceptable outcomes, including fatalities. Recently there has been increasing focus on malicious attack detection and mitigation in SECVNs, and some of this work has considered attacks on sensor data to impact object tracking. Unfortunately, low complexity mitigation approaches which do not compromise performance are lacking. This paper describes an efficient machine-learning enhanced approach for tracking under cyber-attacks. By proper selection of some variances related to the sensor and prior probability density functions, under some assumptions the performance can be made as close as desired to a bound on the best possible performance. However, the complexity of this new approach is dramatically lower than the best existing published low complexity approach, which provides performance which is substantially inferior to that provided by the new approach. The new approach also provides much better scaling with the size of the SECVN. In particular, the complexity increases linearly in the number of sensors, while the best low complexity published approach has a complexity which grows quadratically in the number of sensors. The new approach is also applicable to other tracking applications.

中文翻译：

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优化受网络攻击的配备传感器的联网车辆网络的跟踪性能的算法和分析

配备传感器的联网车辆网络 (SECVN) 有可能通过改进对象跟踪来实现更安全的驾驶，这是 SECVN 中重要的基本构建块。不幸的是，对 SECVN 的网络攻击构成了非常严重的威胁，可能导致不可接受的结果，包括死亡。最近，SECVN 中的恶意攻击检测和缓解越来越受到关注，其中一些工作考虑了对传感器数据的攻击以影响对象跟踪。不幸的是，缺乏不影响性能的低复杂度缓解方法。本文描述了一种有效的机器学习增强方法，用于在网络攻击下进行跟踪。By proper selection of some variances related to the sensor and prior probability density functions, 在某些假设下，性能可以根据需要尽可能接近最佳性能的界限。然而，这种新方法的复杂性显着低于现有的最佳公开低复杂性方法，后者提供的性能大大低于新方法提供的性能。新方法还可以更好地扩展 SECVN 的大小。特别是，复杂度随传感器数量呈线性增加，而最佳的低复杂度公开方法的复杂度随传感器数量呈二次方增长。新方法也适用于其他跟踪应用程序。这种新方法的复杂性显着低于现有的已发表的最佳低复杂性方法，后者提供的性能大大低于新方法提供的性能。新方法还可以更好地扩展 SECVN 的大小。特别是，复杂度随传感器数量呈线性增加，而最佳的低复杂度公开方法的复杂度随传感器数量呈二次方增长。新方法也适用于其他跟踪应用程序。这种新方法的复杂性显着低于现有的已发表的最佳低复杂性方法，后者提供的性能大大低于新方法提供的性能。新方法还可以更好地扩展 SECVN 的大小。特别是，复杂度随传感器数量呈线性增加，而最佳的低复杂度公开方法的复杂度随传感器数量呈二次方增长。新方法也适用于其他跟踪应用程序。而最好的低复杂度已发布方法的复杂度随着传感器数量的增加而呈二次方增长。新方法也适用于其他跟踪应用程序。而最好的低复杂度已发布方法的复杂度随着传感器数量的增加而呈二次方增长。新方法也适用于其他跟踪应用程序。