Intelligent GPS Spoofing Attack Detection in Power Grid

Sabouri, Mohammad Sadegh; Siamak, Sara; Dehghani, Maryam; Mohammadi, Mohsen; Asemani, Mohammad Hassan

doi:10.1109/sgc54087.2021.9664217

Cited by 8 publications

(3 citation statements)

References 23 publications

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“…GPS spoofing attack detection and mitigation using artificial intelligence and machine learning have been considered in the literature in different areas [33,34], but few researches have applied intelligent methods for the GSA detection and correction on PMUs. For example, the work in [35] is to detect the GSA on a power grid by artificial neural networks. The work in [20], deals with a single attack at each time and uses pattern recognition techniques to detect it.…”

Section: Literature Reviewmentioning

confidence: 99%

Increasing the resiliency of power systems in presence of GPS spoofing attacks: A data‐driven deep‐learning algorithm

Sabouri

Siamak

Dehghani

et al. 2023

IET Generation Trans & Dist

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The growing use of wireless technologies in power systems has raised concerns about cybersecurity, particularly regarding GPS spoofing attacks (GSAs). These attacks manipulate GPS data, leading to modifications in the phase angle of phasor measurement units (PMUs). In this paper, a Deep‐learning GPS‐Spoofing Counteraction (DLGSC) algorithm is proposed, utilizing PMU data for GSA detection and PMU data correction. The algorithm incorporates a recurrent neural network (RNN) and a set of long short‐term memory (LSTM) units separately, for signal correction after attack detection. Unlike existing methods that struggle with simultaneous attacks or they are static methods, DLGSC tackles these challenges by leveraging deep learning techniques. By selecting appropriate features for GSA detection, DLGSC achieves accurate results. The algorithm is evaluated on standard IEEE 14‐bus and IEEE 39‐bus power systems, and its performance is compared to statistical, dynamic, and Deep Learning (DL) methods in the literature. Additionally, an experimental setup is designed to validate the algorithm in a laboratory environment. Results demonstrate the easy‐implementable DLGSC algorithm's satisfactory real‐time performance in various scenarios, such as load variations and noise, achieving over 98% accuracy. Notably, DLGSC is cable of detecting multiple GSAs on different PMUs.

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Section: Literature Reviewmentioning

confidence: 99%

Increasing the resiliency of power systems in presence of GPS spoofing attacks: A data‐driven deep‐learning algorithm

Sabouri

Siamak

Dehghani

et al. 2023

IET Generation Trans & Dist

Self Cite

View full text Add to dashboard Cite

show abstract

“…Other detection methods require firmware updates. Among these, there are four works [42], [43], [44], [45] based on the data from power grids. [42] uses the inherent hardware oscillator in power grids as the frequency state reference and does spoofing detection by monitoring the state changes.…”

Section: Table 5 Comparison Of Detection Probabilitymentioning

confidence: 99%

“…[42] uses the inherent hardware oscillator in power grids as the frequency state reference and does spoofing detection by monitoring the state changes. [43] uses the rotor angles of generator buses of power grids as features to train a Neural network. [44] uses multiple features of power grids, such as bus voltage magnitudes, phase angles, and generator speed, to estimate a quasi-dynamic estimation for spoofing classification.…”

Section: Table 5 Comparison Of Detection Probabilitymentioning

confidence: 99%

A Light-Weight Technique to Detect GPS Spoofing Using Attenuated Signal Envelopes

Xiao

Aman

Sikdar

2023

IEEE Open J. Comput. Soc.

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Global Positioning System (GPS) spoofing attacks have attracted more attention as one of the most effective GPS attacks. Since the signals from an authentic satellite and the spoofer undergo different attenuation, the captured envelope of fake GPS signals exhibits distinctive transmission characteristics due to short transmission paths. This can be utilized for GPS spoofing detection. The existing technique for GPS spoofing are either computationally too expensive, require specialize hardware/software updates, or are not accurate enough. To solve these issues, we propose a light-weight GPS spoofing detection method based on a dynamic threshold and captured signal envelope. We validate the proposed technique using experiments based on actual GPS signals and hardware. The relation between envelope characteristics and the distance between a GPS transmitter and receiver are revealed. Inspired by the uncovered relation, a threshold approach towards the detection of GPS spoofing is developed. The proposed approach features a dynamic threshold determined by the dispersion value of a signal envelope's variance instead of a fixed threshold to maximize detection performance in multiple attack scenarios. The results show that the proposed technique can effectively detect GPS spoofing attacks with better accuracy and lower computational complexity as compared to existing techniques.INDEX TERMS Global positioning system, spoofing attacks, intrusion detection, signal evelope.

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A policy-based solution for the detection of colluding GPS-Spoofing attacks in FANETs

Bada

Boubiche

Lagraa

et al. 2021

Transportation Research Part A: Policy and Practice

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Intelligent GPS Spoofing Attack Detection in Power Grid

Cited by 8 publications

References 23 publications

Increasing the resiliency of power systems in presence of GPS spoofing attacks: A data‐driven deep‐learning algorithm

Increasing the resiliency of power systems in presence of GPS spoofing attacks: A data‐driven deep‐learning algorithm

A Light-Weight Technique to Detect GPS Spoofing Using Attenuated Signal Envelopes

A policy-based solution for the detection of colluding GPS-Spoofing attacks in FANETs

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