False Data Injection Attacks in Smart Grids: State of the Art and Way Forward
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866910521907740672 |
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| author | Irfan, Muhammad Sadighian, Alireza Tanveer, Adeen Al-Naimi, Shaikha J. Oligeri, Gabriele |
| author_facet | Irfan, Muhammad Sadighian, Alireza Tanveer, Adeen Al-Naimi, Shaikha J. Oligeri, Gabriele |
| contents | In the recent years cyberattacks to smart grids are becoming more frequent Among the many malicious activities that can be launched against smart grids False Data Injection FDI attacks have raised significant concerns from both academia and industry FDI attacks can affect the internal state estimation processcritical for smart grid monitoring and controlthus being able to bypass conventional Bad Data Detection BDD methods Hence prompt detection and precise localization of FDI attacks is becomming of paramount importance to ensure smart grids security and safety Several papers recently started to study and analyze this topic from different perspectives and address existing challenges Datadriven techniques and mathematical modelings are the major ingredients of the proposed approaches The primary objective of this work is to provide a systematic review and insights into FDI attacks joint detection and localization approaches considering that other surveys mainly concentrated on the detection aspects without detailed coverage of localization aspects For this purpose we select and inspect more than forty major research contributions while conducting a detailed analysis of their methodology and objectives in relation to the FDI attacks detection and localization We provide our key findings of the identified papers according to different criteria such as employed FDI attacks localization techniques utilized evaluation scenarios investigated FDI attack types application scenarios adopted methodologies and the use of additional data Finally we discuss open issues and future research directions |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_10268 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | False Data Injection Attacks in Smart Grids: State of the Art and Way Forward Irfan, Muhammad Sadighian, Alireza Tanveer, Adeen Al-Naimi, Shaikha J. Oligeri, Gabriele Cryptography and Security Signal Processing In the recent years cyberattacks to smart grids are becoming more frequent Among the many malicious activities that can be launched against smart grids False Data Injection FDI attacks have raised significant concerns from both academia and industry FDI attacks can affect the internal state estimation processcritical for smart grid monitoring and controlthus being able to bypass conventional Bad Data Detection BDD methods Hence prompt detection and precise localization of FDI attacks is becomming of paramount importance to ensure smart grids security and safety Several papers recently started to study and analyze this topic from different perspectives and address existing challenges Datadriven techniques and mathematical modelings are the major ingredients of the proposed approaches The primary objective of this work is to provide a systematic review and insights into FDI attacks joint detection and localization approaches considering that other surveys mainly concentrated on the detection aspects without detailed coverage of localization aspects For this purpose we select and inspect more than forty major research contributions while conducting a detailed analysis of their methodology and objectives in relation to the FDI attacks detection and localization We provide our key findings of the identified papers according to different criteria such as employed FDI attacks localization techniques utilized evaluation scenarios investigated FDI attack types application scenarios adopted methodologies and the use of additional data Finally we discuss open issues and future research directions |
| title | False Data Injection Attacks in Smart Grids: State of the Art and Way Forward |
| topic | Cryptography and Security Signal Processing |
| url | https://arxiv.org/abs/2308.10268 |