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A multi-layer perceptron neural network to mitigate the interference of time synchronization attacks in stationary GPS receivers

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Abstract

Accurate timing is one of the key features of the Global Positioning System (GPS), which is employed in many critical infrastructures. Any imprecise time measurement in GPS-based structures, such as smart power grids, economic activities, and communication towers, can lead to disastrous results. The vulnerability of the stationary GPS receivers to the time synchronization attacks (TSAs) jeopardizes the GPS timing precision and trust level. In the past few years, studies suggested the adoption of estimators to follow the authentic trend of the clock offset information under attack conditions. However, the estimators would lose track of the authentic signal without proper knowledge of the signal characteristics. Therefore, a multi-layer perceptron neural network (MLP NN) is proposed to follow the trend of the data. The main difference between the proposed method and typical estimators is the reliance of the network on the training information consisting of signal features. The proposed MLP NN performance has been evaluated through two real-world datasets and two well-known types of TSA. The root mean square error results exhibit an improvement of at least six times compared to other conventional and state-of-art methods.

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Data availability

The first dataset belongs to Lee et al. (2019) and is available at https://github.com/junhwanlee95/Robust-Estimator. The second dataset that verifies the findings of this study is available from the corresponding author upon reasonable request.

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Authors and Affiliations

  1. Department of Electrical Engineering, Iran University of Science and Technology, 16846-13114, Narmak, Tehran, Iran

    N. Orouji & M. R. Mosavi

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  1. N. Orouji
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  2. M. R. Mosavi
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Correspondence to M. R. Mosavi.

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Orouji, N., Mosavi, M.R. A multi-layer perceptron neural network to mitigate the interference of time synchronization attacks in stationary GPS receivers. GPS Solut 25, 84 (2021). https://doi.org/10.1007/s10291-021-01124-z

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