Cited by 11Information and Cyber SecuritySmart Grid Security and ResilienceNetwork Security and Intrusion Detection

TMAP: A Threat Modeling and Attack Path Analysis Framework for Industrial IoT Systems (A Case Study of IoM and IoP)

Open access
Saurabh, Kumar, Gajjala, Deepak, Kaipa, Krishna, Vyas, Ranjana, Vyas, O. P., Khondoker, Rahamatullah·2024·Arabian Journal for Science and Engineering

Integrity signals

Registry

Crossref · 10.1007/s13369-023-08600-3

Cites

1 of 1 references verified0 citation errors · 0 not checkable

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Authors
Saurabh, K., Gajjala, D., Kaipa, K., Vyas, R., Vyas, O. P., & Khondoker, R.
Affiliations
  • Indian Institute of Information Technology Allahabad (IN)
  • Technische Hochschule Mittelhessen (DE)
Corresponding author
Saurabh, Kumar
Venue
Arabian Journal for Science and Engineering
Published
2024-06-18
Volume
49
Issue
9
Pages
13163-13183
ISSN
  • 1319-8025 (ISSN-L)
  • 2193-567X
  • 2191-4281
OpenAlex
W4399783570
Cited by
11
References
1
Citations by year
2026: 2 · 2025: 5 · 2024: 4
FWCI
4.15
Type
journal article
Language
en
Open access
Locations
  • Arabian Journal for Science and Engineering (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • Indian Institute of Information Technology, Allahabad
  • Ministry of Education, India
Topics
  • Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
  • Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
  • Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
Keywords
threat modeling, attack path analysis, Industrial Internet of Things, industrial cyber-physical systems, attack vectors, smart manufacturing, industrial control systems, electric utilities, Internet of Production, smart factory

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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