Sensor attack detection in the presence of transient faults

Sensor attack detection in the presence of transient faults
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DOI:
10.1145/2735960.2735984
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发表时间:
2015-04
期刊:
Proceedings of the ACM/IEEE Sixth International Conference on Cyber-Physical Systems
影响因子:
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通讯作者:
Junkil Park;Radoslav Ivanov;James Weimer;Miroslav Pajic;Insup Lee
Junkil Park;Radoslav Ivanov;James Weimer;Miroslav Pajic;Insup Lee
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其他
文献类型:
--
作者:
Junkil Park;Radoslav Ivanov;James Weimer;Miroslav Pajic;Insup Lee

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本文讨论的问题,检测和识别的传感器攻击中存在的瞬态故障。我们考虑一个系统,多个传感器测量相同的物理变量,其中一些传感器可能受到攻击,并提供恶意值。我们考虑一个设置,其中每个传感器为控制器提供了一个区间的可能值的真值。虽然存在用于检测恶意传感器攻击的方法,但是它们是保守的,因为它们以相同的方式处理攻击和故障,因此忽略了传感器有时可能由于临时干扰(例如,GPS的隧道)。为了解决这个问题,我们提出了一个瞬态故障模型为每个传感器和一个算法,旨在检测和识别攻击的瞬态故障的存在。故障模型由三个方面组成:传感器间隔的大小(1)和在给定窗口大小(3)中允许的错误数量的上限(2)。给定每个传感器的这种模型,该算法使用传感器之间的成对不一致来检测和识别攻击。除了该算法,我们提供了一个框架,为每个传感器的训练数据的基础上选择一个故障模型。最后,我们验证了该算法的性能真实的测量数据从无人驾驶地面车辆。
This paper addresses the problem of detection and identification of sensor attacks in the presence of transient faults. We consider a system with multiple sensors measuring the same physical variable, where some sensors might be under attack and provide malicious values. We consider a setup, in which each sensor provides the controller with an interval of possible values for the true value. While approaches exist for detecting malicious sensor attacks, they are conservative in that they treat attacks and faults in the same way, thus neglecting the fact that sensors may provide faulty measurements at times due to temporary disturbances (e.g., a tunnel for GPS). To address this problem, we propose a transient fault model for each sensor and an algorithm designed to detect and identify attacks in the presence of transient faults. The fault model consists of three aspects: the size of the sensor's interval (1) and an upper bound on the number of errors (2) allowed in a given window size (3). Given such a model for each sensor, the algorithm uses pairwise inconsistencies between sensors to detect and identify attacks. In addition to the algorithm, we provide a framework for selecting a fault model for each sensor based on training data. Finally, we validate the algorithm's performance on real measurement data obtained from an unmanned ground vehicle.