TY - JOUR
T1 - Impact of the Industrial Internet of Things (IIoT) on Cybersecurity within Industry 4.0
T2 - 23rd LACCEI International Multi-Conference for Engineering, Education and Technology, LACCEI 2025
AU - Sánchez Rosas, Luis Junior
AU - Vega Solis, Edwin Rolando
AU - Paico Egusquiza, Ayle Jarumy
AU - Mendoza Vasquez, Ari Anielka
N1 - Publisher Copyright:
© 2025 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.
PY - 2025
Y1 - 2025
N2 - The accelerated growth of the Industrial Internet of Things (IIoT) has driven the need for advanced and secure anomaly detection solutions, especially in industrial environments where cybersecurity is critical. This study provides a Systematic Literature Review (SLR) guided by the PRISMA 2020 method, with the objective of identifying the impact of IIoT on cybersecurity within Industry 4.0. Thirty-two studies published between 2020 and 2024 in academic databases such as Scopus and Web of Science were reviewed. The results reveal that emerging technologies such as Blockchain, Machine Learning and Deep Learning are playing a central role in data protection and intrusion detection in IIoT systems. Blockchain has proven to be effective in ensuring data integrity and improving operational efficiency. This review highlights the importance of adopting robust cybersecurity solutions to mitigate risks and strengthen resilience in Industry 4.0 and suggests key areas for future research in this field.
AB - The accelerated growth of the Industrial Internet of Things (IIoT) has driven the need for advanced and secure anomaly detection solutions, especially in industrial environments where cybersecurity is critical. This study provides a Systematic Literature Review (SLR) guided by the PRISMA 2020 method, with the objective of identifying the impact of IIoT on cybersecurity within Industry 4.0. Thirty-two studies published between 2020 and 2024 in academic databases such as Scopus and Web of Science were reviewed. The results reveal that emerging technologies such as Blockchain, Machine Learning and Deep Learning are playing a central role in data protection and intrusion detection in IIoT systems. Blockchain has proven to be effective in ensuring data integrity and improving operational efficiency. This review highlights the importance of adopting robust cybersecurity solutions to mitigate risks and strengthen resilience in Industry 4.0 and suggests key areas for future research in this field.
KW - Blockchain
KW - Cybersecurity
KW - IIoT
KW - Industry 4.0
KW - IoT
UR - https://www.scopus.com/pages/publications/105019318340
U2 - 10.18687/LACCEI2025.1.1.1647
DO - 10.18687/LACCEI2025.1.1.1647
M3 - Conference article
AN - SCOPUS:105019318340
SN - 2414-6390
JO - Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
JF - Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
IS - 2025
Y2 - 16 July 2025 through 18 July 2025
ER -