セキュアなサイバー・フィジカル・システム:マルコヴィアン・チェーンベースのFDI攻撃の識別と緩和戦略
Zahra Rasooli Berardehi1, Juliang Yin2, Mostafa Taheri1
1School of Mathematics and Computer Science, Guangzhou University, Guangzhou 510006, China.
ISA transactions
|February 20, 2026
まとめ
この研究では,偽データインジェクション攻撃 (FDIA) からサイバー物理システム (CPS) を保護するための新しい防御技術が紹介されています. この方法は,拡張状態のオブザーバーと,セキュリティと安定性を高めるために,PPOチューニング付きの超ローカルモデル制御を使用しています.
科学分野:
- サイバー物理システムのセキュリティーセキュリティー
- 制御理論 制御理論
- 機械学習のアプリケーション
背景:
- サイバー物理システム (CPS) は,偽データ注入攻撃 (FDIA) のようなサイバー脅威に対してますます脆弱になっています.
- FDIAは,システムの安定性を妨害し,重要なコンポーネントや安全メカニズムに影響を及ぼします.
- 既存の防御メカニズムは,FDIAの予測不可能な性質を適切に対処できない可能性があります.
研究 の 目的:
- CPSにおけるFDIAを特定し,軽減するための新しい防衛技術を開発する.
- 洗練されたサイバー脅威に対するCPSのセキュリティと安定性を高めること.
- 現在の方法より性能が優れている堅固なソリューションを提供するためです.
主な方法:
- マルコビアンジャンププロセス (MJP) を使用してFDIAをモデリングする.
- FDIA検出のために拡張状態オブザーバー (ESO) を採用します.
- 適応的緩和のための近接政策最適化 (PPO) アルゴリズムによる超ローカルモデルの制御を利用する.
主要な成果:
- 提案された防衛計画は,FDIAを効果的に特定し,軽減します.
- PPO調節されたコントローラは,CPIとANNベースのカルマンフィルターと比較して優れたパフォーマンスを示しました.
- 既存の方法と比較して,約70%および100%の大幅なパフォーマンスの改善が達成されました.
結論:
- この新しい防御技術は,CPSをFDIAから守るための強力な解決策を提供します.
- ESO,ultra-local control,およびPPOの統合により,適応的かつ効果的な脅威緩和が提供されます.
- このアプローチは,CPSのセキュリティと運用の安定性を大幅に高めます.
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