IoT対応のスマートシティシステムにおけるオープンデータのサイバーセキュリティベースラインとリスク軽減:Hradec Kralove Regionのケーススタディ
Vladimir Sobeslav1, Josef Horalek1
1Faculty of Informatics and Management, University of Hradec Kralove, 500 03 Hradec Kralove, Czech Republic.
Sensors (Basel, Switzerland)
|August 28, 2025
まとめ
この研究は,重要なデータフローと影響を特定するために自動化された分析を使用して,スマートシティにおけるオープンデータのサイバーセキュリティリスクモデルを提示します. 都市デジタルインフラに優先的にデータ保護措置を講じています.
科学分野:
- サイバーセキュリティ
- スマートシティ・テクノロジー
- データ管理
背景:
- スマートシティのイニシアチブは,オープンデータに依存し,ユニークなサイバーセキュリティの課題を導入しています.
- 機密情報を保護し,サービスの継続性を確保するには,効果的なリスクモデリングが不可欠です.
研究 の 目的:
- スマートシティ環境におけるオープンデータのサイバーセキュリティリスクモデリングの枠組みを開発し,検証する.
- 自動化分析とビジネスインパクト分析 (BIA) を通じてサイバーセキュリティのベースラインを確立する.
主な方法:
- プロセスマッピングと自動化された分析のために拡張されたビジネスプロセスモデルとノテーション (BPMN) を利用しました.
- 混乱の影響 (RTO,MTPD,MTDL) を定量化するための統合ビジネスインパクト分析 (BIA)
- リスク軽減戦略の自動選択のための枠組みを開発しました.
主要な成果:
- 重要なデータフローを特定し,障害に対する耐性を定量化しました.
- オープンデータでの可視化-公開プロセスは,中断に対して高い耐性を示したが,強力な機密性と完全性を要求した.
- 定義された最大容認可能なデータ損失 (MTDL) は24時間であり,特定のセキュリティ対策の勧告 (暗号化,アクセス制御,バックアップ) を導きます.
結論:
- プロセスマッピングとセキュリティ要件を組み合わせることで,スマートシティのデータ保護を効果的に優先します.
- 提案された方法論は,データの可用性と整合性を高め,レジリエントな都市デジタルインフラに寄与します.
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