Cyber attacks on estimation sensor networks and iots: Impact, mitigation and implications to unattacked systems
Jiangfan Zhang, Rick S. Blum, Lance M. Kaplan
Abstract
Estimation of an unknown deterministic vector from quantized sensor data is considered in the presence of spoofing and man-in-the-middle attacks. First, asymptotically optimum processing, which identifies and categorizes the attacked sensors into different groups according to distinct types of attacks, is outlined in the face of man-in-the-middle attacks. Necessary and sufficient conditions are provided under which utilizing the attacked sensor data will lead to better estimation performance when compared to approaches where the attacked sensors are ignored. Next, necessary and sufficient conditions are provided under which spoofing attacks provide a guaranteed attack performance in terms of the Cramer-Rao Bound regardless of the processing the estimation system employs. It is shown that it is always possible to construct such a highly desirable attack by properly employing an attack vector parameter having a sufficiently large dimension relative to the number of quantization levels employed, which was not observed previously. For unattacked quantized estimation systems, a general limitation on the dimension of a vector parameter which can be accurately estimated is uncovered.
BibTeX
@inproceedings{icassp2017_cyberattacksones,
title = {Cyber attacks on estimation sensor networks and iots: Impact, mitigation and implications to unattacked systems},
author = {Jiangfan Zhang and Rick S. Blum and Lance M. Kaplan},
booktitle = {ICASSP 2017},
year = {2017}
}