CYBER-PHYSICAL SYSTEMS RISK ASSESSMENT VIA KNOWLEDGE-BASED CYBERSECURITY AUTOMATION

Cyber-Physical Systems, often known as CPS, are considered to be essential components of the modern infrastructure. Among these, you can find networks for healthcare and energy. More complex and linked cyberattacks increase the likelihood that they would interrupt services and cause physical harm. Conventional methods of risk assessment limit our ability to adapt to these evolving dangers. An innovative cybersecurity automation tool is proposed as a solution to this challenge in this research. It is easier to assess risks, determine their impacts, and respond situationally in real time when ontological reasoning, artificial intelligence, and threat intelligence technologies are combined. The technology elevates people's situational awareness and decision-making capabilities through the integration of machine learning with expert security expertise. A fictitious case research illustrates the ways in which smart grid technology enhances safety, enables better response to hazards, and identifies problems. With this fresh approach, CPS might strengthen its defenses and successfully lessen cyberthreats in an ever-evolving digital landscape.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-08-13
DOI
https://doi.org/10.5281/zenodo.16831345
Primary Topic
Smart Grid Security and Resilience
Type
article
Field-Weighted Citation Impact
0.00
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article

CYBER-PHYSICAL SYSTEMS RISK ASSESSMENT VIA KNOWLEDGE-BASED CYBERSECURITY AUTOMATION

Zenodo (CERN European Organization for Nuclear Research)
Smart Grid Security and Resilience
article

CYBER-PHYSICAL SYSTEMS RISK ASSESSMENT VIA KNOWLEDGE-BASED CYBERSECURITY AUTOMATION

article en

Abstract

Cyber-Physical Systems, often known as CPS, are considered to be essential components of the modern infrastructure. Among these, you can find networks for healthcare and energy. More complex and linked cyberattacks increase the likelihood that they would interrupt services and cause physical harm. Conventional methods of risk assessment limit our ability to adapt to these evolving dangers. An innovative cybersecurity automation tool is proposed as a solution to this challenge in this research. It is easier to assess risks, determine their impacts, and respond situationally in real time when ontological reasoning, artificial intelligence, and threat intelligence technologies are combined. The technology elevates people's situational awareness and decision-making capabilities through the integration of machine learning with expert security expertise. A fictitious case research illustrates the ways in which smart grid technology enhances safety, enables better response to hazards, and identifies problems. With this fresh approach, CPS might strengthen its defenses and successfully lessen cyberthreats in an ever-evolving digital landscape.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 46%
Smart Grid Security and Resilience
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