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| ■論文No. |
CMN24029 |
| ■ページ数 |
6ページ |
■発行日
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2024/03/25 |
| ■タイトル |
An approach to select and schedule the most appropriate sequence of security patches, for a large set of vulnerabilities in a power generation system, using game theory and reinforcement learning |
| ■タイトル(英語) |
An approach to select and schedule the most appropriate sequence of security patches, for a large set of vulnerabilities in a power generation system, using game theory and reinforcement learning |
| ■著者名 |
Minz Remish Leonard(Hitachi India Pvt. Ltd.),Yadav Geeta(Indian Institute of Technology, Ropar),Kalle Ritesh Kumar(Hitachi India Pvt. Ltd.) |
| ■著者名(英語) |
Remish Leonard Minz(Hitachi India Pvt. Ltd.),Geeta Yadav(Indian Institute of Technology, Ropar),Ritesh Kumar Kalle(Hitachi India Pvt. Ltd.) |
| ■価格 |
会員 ¥220 一般 ¥330 |
| ■書籍種類 |
研究会(論文単位) |
| ■グループ名 |
【C】電子・情報・システム部門 通信研究会 |
| ■本誌 |
2024年3月28日-2024年3月29日通信研究会
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| ■本誌掲載ページ |
67-72ページ |
| ■原稿種別 |
英語 |
| ■電子版へのリンク |
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| ■キーワード |
Power grids|Security|Vulnerability|Countermeasures|Game Theory|Reinforcement Learning|Power grids|Security|Vulnerability|Countermeasures|Game Theory|Reinforcement Learning |
| ■要約(日本語) |
The surge in cyberattacks on power grids emphasizes
the need for protecting critical assets and ensuring power
reliability. However, maintaining reliability requires promptly
addressing vulnerabilities through security measures, which may
cause system downtime. Power demand requirements impose
strict constraints on grid up-time, creating a trade-off between
grid security and power availability. We propose a two stage
optimization method to minimize this trade-off for a power
generation plant. Firstly, we employ a game theory approach
to prioritize the vulnerabilities for applying security countermeasures
in the generation plant. Second, we use power demand
and supply data of an Indian city to forecast future Opportunity
Window and use Reinforcement Learning to efficiently utilize
the Opportunity Window to patch the prioritized vulnerabilities
from the first stage. We evaluate this approach on supervisory
control system of a power generation plant. |
| ■要約(英語) |
The surge in cyberattacks on power grids emphasizes
the need for protecting critical assets and ensuring power
reliability. However, maintaining reliability requires promptly
addressing vulnerabilities through security measures, which may
cause system downtime. Power demand requirements impose
strict constraints on grid up-time, creating a trade-off between
grid security and power availability. We propose a two stage
optimization method to minimize this trade-off for a power
generation plant. Firstly, we employ a game theory approach
to prioritize the vulnerabilities for applying security countermeasures
in the generation plant. Second, we use power demand
and supply data of an Indian city to forecast future Opportunity
Window and use Reinforcement Learning to efficiently utilize
the Opportunity Window to patch the prioritized vulnerabilities
from the first stage. We evaluate this approach on supervisory
control system of a power generation plant. |
| ■版 型 |
A4 |
| ■PDFファイルサイズ |
1,345Kバイト |
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