Optimal False Data Injection Attacks on MTC

doi:10.1109/TVT.2021.3139337

Optimal False Data Injection Attacks on MTC

2022-03-01

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DOI：10.1109/TVT.2021.3139337

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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.

Keyword:

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

Optimal False Data Injection Attacks on MTC

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