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Optimal False Data Injection Attacks on MTC
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AI
DOI：10.1109/TVT.2021.3139337.png)
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Abstract
En 中文
This paper proposes a convex optimization problem, based on which the optimal strategy of false data injection (FDI) attacks is obtained to intrude machine-type-communications (MTC) networks from the perspective of an attacker, aiming to seek effective defensive measures based on a good understanding of attackers' behaviour. We consider a target tracking example, which is a typical application of MTC networks. Specifically, as a type of MTC devices, smart sensors each have the ability of perception, calculation and communication and all of them can form a sensor network. In this network, its sensors and transmission channels are vulnerable to FDI attacks, resulting in the degradation of system estimation performance. In order to maximize the estimation error covariance of MTC network, the attacker needs to decide which sensors and channels to intrude due to limited energy budget. The estimation error covariance of the MTC network is calculated, based on which a convex optimization problem to obtain the optimal attack strategy is proposed. Simulation results demonstrate that the optimal attack strategy maximizes the transient mean-square deviation and estimation error covariance of the MTC network.
Keywords:
Estimation error
Channel estimation
Technological innovation
Noise measurement
Wireless sensor networks
Wireless communication
Time measurement
Estimation error covariance
false data injection attacks
machine-type-communications
optimal attack strategy
target tracking
Journal
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7.1
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1.8W
Citations:
6.6W