Modified Matrix Completion-based Detection of Stealthy Data Manipulation Attacks in Low Observable Distribution Systems
A composite detection technique against stealthy data manipulations is developed in this paper for distribution networks that are low observable. Attack detection strategies typically rely on state estimation which becomes challenging when limited measurements are available. In this paper, a modifie...
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| Veröffentlicht in: | IEEE transactions on smart grid 2023-11, Vol.14 (6), p.1-1 |
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| creator | Rajasekaran, James Ranjith Kumar Natarajan, Balasubramaniam Pahwa, Anil |
| description | A composite detection technique against stealthy data manipulations is developed in this paper for distribution networks that are low observable. Attack detection strategies typically rely on state estimation which becomes challenging when limited measurements are available. In this paper, a modified matrix completion approach provides estimates of the system state and its error variances for the locations in the network where measurements are unavailable. Using the error statistics and their corresponding state estimates, bad data detection can be carried out using the chi-squared test. The proposed approach employs a moving target defence strategy (MTD) where the network parameters are perturbed through distributed flexible AC transmission system (D-FACTS) devices such that stealthy data manipulation attacks can be exposed in the form of bad data. Thus, the bad data detection approach developed in this paper can detect stealthy attacks using the MTD strategy. This technique is implemented on 37-bus and 123-bus three-phase unbalanced distribution networks to demonstrate the attack detection accuracy even for a low observable system. |
| doi_str_mv | 10.1109/TSG.2023.3266834 |
| format | Article |
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Attack detection strategies typically rely on state estimation which becomes challenging when limited measurements are available. In this paper, a modified matrix completion approach provides estimates of the system state and its error variances for the locations in the network where measurements are unavailable. Using the error statistics and their corresponding state estimates, bad data detection can be carried out using the chi-squared test. The proposed approach employs a moving target defence strategy (MTD) where the network parameters are perturbed through distributed flexible AC transmission system (D-FACTS) devices such that stealthy data manipulation attacks can be exposed in the form of bad data. Thus, the bad data detection approach developed in this paper can detect stealthy attacks using the MTD strategy. 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Attack detection strategies typically rely on state estimation which becomes challenging when limited measurements are available. In this paper, a modified matrix completion approach provides estimates of the system state and its error variances for the locations in the network where measurements are unavailable. Using the error statistics and their corresponding state estimates, bad data detection can be carried out using the chi-squared test. The proposed approach employs a moving target defence strategy (MTD) where the network parameters are perturbed through distributed flexible AC transmission system (D-FACTS) devices such that stealthy data manipulation attacks can be exposed in the form of bad data. Thus, the bad data detection approach developed in this paper can detect stealthy attacks using the MTD strategy. This technique is implemented on 37-bus and 123-bus three-phase unbalanced distribution networks to demonstrate the attack detection accuracy even for a low observable system.</description><subject>Bad Data detection</subject><subject>Chi-square test</subject><subject>Corresponding states</subject><subject>Covariance matrices</subject><subject>Distribution networks</subject><subject>Distribution system</subject><subject>Error analysis</subject><subject>Estimates</subject><subject>Flexible AC power transmission systems</subject><subject>Matrix completion</subject><subject>Measurement uncertainty</subject><subject>Moving Target Defence</subject><subject>Moving targets</subject><subject>Noise measurement</subject><subject>Perturbation methods</subject><subject>State estimation</subject><subject>Training</subject><issn>1949-3053</issn><issn>1949-3061</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkDtPwzAUhS0EElXpzsBgiTnFryTOWLVQkFp1aJktx7GFS5oE26H03-M-hLjLfX3nXukAcI_RGGNUPG3W8zFBhI4pyTJO2RUY4IIVCUUZvv6rU3oLRt5vUQxKaUaKAdgv28oaqyu4lMHZHzhtd12tg22bpJQ-zmc6aHXsYWvgOmhZh48DnMkgo6SxXV_L03YSglSfHtoGLto9XJVeu29Z1hrOrI-ny_6ErQ8-6J2_AzdG1l6PLnkI3l-eN9PXZLGav00ni0QRloaEklQrlWGDUy5NpZkyOUlZpQzKCZEsx5whXplM0QoTTnkuGTZIGiZRzitMh-DxfLdz7VevfRDbtndNfCkIzwvKcc5JpNCZUq713mkjOmd30h0ERuLosIgOi6PD4uJwlDycJVZr_Q_HEcIZ_QUwJ3ho</recordid><startdate>20231101</startdate><enddate>20231101</enddate><creator>Rajasekaran, James Ranjith Kumar</creator><creator>Natarajan, Balasubramaniam</creator><creator>Pahwa, Anil</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| source | IEEE Electronic Library (IEL) |
| subjects | Bad Data detection Chi-square test Corresponding states Covariance matrices Distribution networks Distribution system Error analysis Estimates Flexible AC power transmission systems Matrix completion Measurement uncertainty Moving Target Defence Moving targets Noise measurement Perturbation methods State estimation Training |
| title | Modified Matrix Completion-based Detection of Stealthy Data Manipulation Attacks in Low Observable Distribution Systems |
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