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Risk Modeling for Cyber-Physical System Security Planning
Risk Modeling for Cyber-Physical System Security Planning
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202104835
- ISBN
- 9798290924618
- DDC
- 621
- 서명/저자
- Risk Modeling for Cyber-Physical System Security Planning
- 발행사항
- [Sl] : The University of Wisconsin - Madison, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 98 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-02, Section: B.
- 주기사항
- Advisor: Albert, Laura A.
- 학위논문주기
- Thesis (Ph.D.)--The University of Wisconsin - Madison, 2025.
- 초록/해제
- 요약Our society depends on critical infrastructures increasingly composed of interdependent cyber-physical systems (CPS). Securing these infrastructures requires understanding the dynamic interactions across cyber, physical, and human domains, as well as how adversaries can exploit these interdependencies. In this work, I present a risk modeling framework for CPS security that leverages attack graphs and directed acyclic graphs (DAG) to characterize system vulnerabilities and adversarial pathways using a layered approach. Motivated by the temporal and stochastic nature of CPS operations, I incorporate discrete-time Markov chains (DTMC) to enable time-dependent risk analysis.The structured modeling framework for CPS security integrates attack graphs with operational process models to support risk-informed decision-making. I apply this framework to two case studies in critical infrastructure: (1) a cyber-physical-human system (CPHS) representing the vote-by-mail (VBM) election process, and (2) a cyber-physical energy system (CPES) incorporating a small modular reactor (SMR). A time-inhomogeneous discrete-time Markov chain (DTMC) model is used for VBM risk modeling to capture ballot flow, adversarial interference, and policy mitigations with a case study using absentee voting data from Milwaukee, WI. The SMR station has a layered architecture and construct attack graphs across functional layers to represent potential attack paths and system interdependencies. I introduce new metrics to assess vulnerability and component criticality under adversarial conditions. By framing these systems as CPES/CPHS, the framework effectively bridges the gap between abstract threat modeling and tangible operational realities, ensuring a more robust understanding of potential vulnerabilities. Together, these case studies demonstrate how temporal and structural extensions of attack graphs can provide scalable and interpretable tools for CPS risk analysis and prescriptive security planning.
- 일반주제명
- Energy
- 일반주제명
- Electrical engineering
- 일반주제명
- Computer engineering
- 키워드
- Informed policy
- 키워드
- Risk modeling
- 키워드
- Security
- 기타저자
- The University of Wisconsin - Madison Electrical and Computer Engineering
- 기본자료저록
- Dissertations Abstracts International. 87-02B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520260202104835
■006m o d
■007cr#unu||||||||
■020 ▼a9798290924618
■035 ▼a(MiAaPQ)AAI32171407
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a621
■1001 ▼aHaseltine, Carmen.
■24510▼aRisk Modeling for Cyber-Physical System Security Planning
■260 ▼a[Sl]▼bThe University of Wisconsin - Madison▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a98 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-02, Section: B.
■500 ▼aAdvisor: Albert, Laura A.
■5021 ▼aThesis (Ph.D.)--The University of Wisconsin - Madison, 2025.
■520 ▼aOur society depends on critical infrastructures increasingly composed of interdependent cyber-physical systems (CPS). Securing these infrastructures requires understanding the dynamic interactions across cyber, physical, and human domains, as well as how adversaries can exploit these interdependencies. In this work, I present a risk modeling framework for CPS security that leverages attack graphs and directed acyclic graphs (DAG) to characterize system vulnerabilities and adversarial pathways using a layered approach. Motivated by the temporal and stochastic nature of CPS operations, I incorporate discrete-time Markov chains (DTMC) to enable time-dependent risk analysis.The structured modeling framework for CPS security integrates attack graphs with operational process models to support risk-informed decision-making. I apply this framework to two case studies in critical infrastructure: (1) a cyber-physical-human system (CPHS) representing the vote-by-mail (VBM) election process, and (2) a cyber-physical energy system (CPES) incorporating a small modular reactor (SMR). A time-inhomogeneous discrete-time Markov chain (DTMC) model is used for VBM risk modeling to capture ballot flow, adversarial interference, and policy mitigations with a case study using absentee voting data from Milwaukee, WI. The SMR station has a layered architecture and construct attack graphs across functional layers to represent potential attack paths and system interdependencies. I introduce new metrics to assess vulnerability and component criticality under adversarial conditions. By framing these systems as CPES/CPHS, the framework effectively bridges the gap between abstract threat modeling and tangible operational realities, ensuring a more robust understanding of potential vulnerabilities. Together, these case studies demonstrate how temporal and structural extensions of attack graphs can provide scalable and interpretable tools for CPS risk analysis and prescriptive security planning.
■590 ▼aSchool code: 0262.
■650 4▼aEnergy
■650 4▼aElectrical engineering
■650 4▼aComputer engineering
■653 ▼aInformed policy
■653 ▼aInfrastructure protection
■653 ▼aRisk modeling
■653 ▼aSecurity
■653 ▼aStochastic process
■690 ▼a0796
■690 ▼a0791
■690 ▼a0544
■690 ▼a0464
■71020▼aThe University of Wisconsin - Madison▼bElectrical and Computer Engineering.
■7730 ▼tDissertations Abstracts International▼g87-02B.
■790 ▼a0262
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17359106▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
