hig.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard-cite-them-right
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • sv-SE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • de-DE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
The Use of Technology and Machine Learning in Assessing Cybersecurity of Environmental Sustainability
University of Memphis.
University of Gävle, Faculty of Education and Business Studies, Department of Business and Economic Studies, Business administration.ORCID iD: 0000-0002-2536-0446
2024 (English)Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

This paper examines the crucial link between corporate governance and cybersecurity risk assessment within the realm of environmental sustainability. With the rise in cyberattacks, organizations face significant challenges in protecting sensitive ESG-related data. The integration of machine learning (ML) into cybersecurity offers new opportunities for improving risk management and preventing data breaches. The study introduces frameworks like the MEREC-K-means method to assess cybersecurity risks in environmental contexts, demonstrating how ML models can identify vulnerabilities and enhance resilience in sustainability efforts. By analyzing industry-specific data, the research identifies key cybersecurity threats and provides strategic recommendations for aligning sustainability and cybersecurity goals. The findings highlight the importance of board oversight in ensuring that cybersecurity strategies support environmental objectives, protecting ESG performance from emerging cyber threats. This paper adds to the growing body of literature on using ML to safeguard sustainability, offering insights on mitigating cyber risks while advancing environmental goals.

Place, publisher, year, edition, pages
Elsevier, 2024.
National Category
Business Administration
Research subject
Intelligent Industry
Identifiers
URN: urn:nbn:se:hig:diva-45828OAI: oai:DiVA.org:hig-45828DiVA, id: diva2:1905305
Conference
7th Cryptocurrency Research Conference (CRC 2024) The Zayed University Convention Centre, Dubai Campus, UAE September 23-24, 2024
Available from: 2024-10-13 Created: 2024-10-13 Last updated: 2025-10-02Bibliographically approved

Open Access in DiVA

fulltext(929 kB)77 downloads
File information
File name FULLTEXT01.pdfFile size 929 kBChecksum SHA-512
6c65f7cf35ab6b26b277de937c89b1ee439dd0d60118c947adcb675eb60b61b49d3a6aebdfed2ed04be82426dbbd0800016efacc9742ef2475dfd8e50f8b1b10
Type fulltextMimetype application/pdf
fulltext(774 kB)168 downloads
File information
File name FULLTEXT02.pdfFile size 774 kBChecksum SHA-512
4355012066b5f1e3be71d463ed54f2deae82281d6a85e77780ccc9f2bcc81463484e996f7dd3ab36c7e91d139a7eb0c3843e62cb1487ac6b7f51e297566a1e3a
Type fulltextMimetype application/pdf

Authority records

Homayoun, Saeid

Search in DiVA

By author/editor
Homayoun, Saeid
By organisation
Business administration
Business Administration

Search outside of DiVA

GoogleGoogle Scholar
Total: 247 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 197 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard-cite-them-right
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • sv-SE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • de-DE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf