TIIS (한국인터넷정보학회)
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| 한글제목(Korean Title) |
Multi Label Deep Learning classification approach for False Data Injection Attacks in Smart Grid |
| 영문제목(English Title) |
Multi Label Deep Learning classification approach for False Data Injection Attacks in Smart Grid |
| 저자(Author) |
Prasanna Srinivasan.V
Balasubadra.K
Saravanan.K
Arjun.V.S
Malarkodi.S
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| 원문수록처(Citation) |
VOL 15 NO. 06 PP. 2168 ~ 2187 (2021. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
The smart grid replaces the traditional power structure with information inventiveness that contributes to a new physical structure. In such a field, malicious information injection can potentially lead to extreme results. Incorrect, FDI attacks will never be identified by typical residual techniques for false data identification. Most of the work on the detection of FDI attacks is based on the linearized power system model DC and does not detect attacks from the AC model. Also, the overwhelming majority of current FDIA recognition approaches focus on FDIA, whilst significant injection location data cannot be achieved. Building on the continuous developments in deep learning, we propose a Deep Learning based Locational Detection technique to continuously recognize the specific areas of FDIA. In the development area solver gap happiness is a False Data Detector (FDD) that incorporates a Convolutional Neural Network (CNN). The FDD is established enough to catch the fake information. As a multi-label classifier, the following CNN is utilized to evaluate the irregularity and cooccurrence dependency of power flow calculations due to the possible attacks. There are no earlier statistical assumptions in the architecture proposed, as they are "model-free." It is also "cost-accommodating" since it does not alter the current FDD framework and it is only several microseconds on a household computer during the identification procedure. We have shown that ANN-MLP, SVM-RBF, and CNN can conduct locational detection under different noise and attack circumstances through broad experience in IEEE 14, 30, 57, and 118 bus systems. Moreover, the multi-name classification method used successfully improves the precision of the present identification. |
| 키워드(Keyword) |
Deep Learning
False Data Injection Attack
Internet of Things
Machine Learning
Multi-label classification
Power System
Smart Grid
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| 파일첨부 |
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