Function-driven cyber-physical security in smart manufacturing

Abstract As manufacturing evolves with cyber-physical systems (CPSs), ensuring security across all components—no matter how small—is critical to preventing system-wide failures. While frameworks like Reference Architectural Model Industrie 4.0 (RAMI 4.0) structure industrial processes, they overlook security, leaving CPS vulnerable to cyber-physical threats. The increasing convergence of Information Technology and Operational Technology has further expanded the attack surface, exposing industrial automation to new vulnerabilities. This study investigates how security risks in automated manufacturing systems can be managed. Our study systematically reviews the literature, employing Information System Security Risk Management-based risk assessment and threat analysis to identify gaps in existing security frameworks. To bridge these gaps, we introduce the functions, assets, security threats, and mitigation techniques (FAST) method for assessing and mitigating security risks in CPS-driven manufacturing. Our findings highlight underexplored vulnerabilities across RAMI 4.0 layers, emphasising the need for security frameworks tailored to industrial automation. The FAST method provides a structured and scalable way to CPS security, ensuring that security measures integrate seamlessly with industrial operations while addressing previously overlooked vulnerabilities.

Authors

Institutions

Publication Details

Journal
Software & Systems Modeling
Published
2026-04-01
DOI
https://doi.org/10.1007/s10270-026-01370-0
Primary Topic
Smart Grid Security and Resilience
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Function-driven cyber-physical security in smart manufacturing

Raimundas Matulevičius, Vjatšeslav Antipenko
Software & Systems Modeling
Smart Grid Security and Resilience
article

Function-driven cyber-physical security in smart manufacturing

Raimundas Matulevičius, Vjatšeslav Antipenko
article en

Abstract

Abstract As manufacturing evolves with cyber-physical systems (CPSs), ensuring security across all components—no matter how small—is critical to preventing system-wide failures. While frameworks like Reference Architectural Model Industrie 4.0 (RAMI 4.0) structure industrial processes, they overlook security, leaving CPS vulnerable to cyber-physical threats. The increasing convergence of Information Technology and Operational Technology has further expanded the attack surface, exposing industrial automation to new vulnerabilities. This study investigates how security risks in automated manufacturing systems can be managed. Our study systematically reviews the literature, employing Information System Security Risk Management-based risk assessment and threat analysis to identify gaps in existing security frameworks. To bridge these gaps, we introduce the functions, assets, security threats, and mitigation techniques (FAST) method for assessing and mitigating security risks in CPS-driven manufacturing. Our findings highlight underexplored vulnerabilities across RAMI 4.0 layers, emphasising the need for security frameworks tailored to industrial automation. The FAST method provides a structured and scalable way to CPS security, ensuring that security measures integrate seamlessly with industrial operations while addressing previously overlooked vulnerabilities.

Software & Systems Modeling
University of Tartu (EE)
European Commission, Research Executive Agency
Openalex Percentile: Top 31%
Smart Grid Security and Resilience
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.