Industrial Internet of Things Ecosystems Security and Digital Forensics: Achievements, Open Challenges, and Future Directions
2024 (English)In: ACM Computing Surveys, ISSN 0360-0300, E-ISSN 1557-7341, Vol. 56, no 5, article id 131Article in journal (Refereed) Published
Abstract [en]
The Industrial Internet of Things (IIoT) has been positioned as a key pillar of the Industry 4.0 revolution, which is projected to continue accelerating and realizing digital transformations. The IIoT is becoming indispensable, providing the means through which modern communication is conducted across industries and offering improved efficiency, scalability, and robustness. However, the structural and dynamic complexity introduced by the continuous integration of the IIoT has widened the scope for cyber-threats, as the processes and data generated by this integration are susceptible and vulnerable to attacks. This article presents an in-depth analysis of the state-of-the-art in the IIoT ecosystem from security and digital forensics perspectives. The dimensions of this study are twofold: first, we present an overview of the cutting-edge security of IIoT ecosystems, and second, we survey the literature on digital forensics. The key achievements, open challenges, and future directions are identified in each case. The challenges and directions for future studies that we identify will provide important guidance for cybersecurity researchers and practitioners. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.
Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2024. Vol. 56, no 5, article id 131
Keywords [en]
digital forensics, ecosystem, Industrial internet of things (IIoT), security, Cybersecurity, Data integration, Electronic crime countermeasures, Internet of things, Continuous integrations, Cyber threats, Digital transformation, Dynamic complexity, Efficiency scalability, In-depth analysis, Industrial internet of thing, State of the art, Structural complexity, Ecosystems
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-25974DOI: 10.1145/3635030ISI: 001168549500024Scopus ID: 2-s2.0-85184143595OAI: oai:DiVA.org:bth-25974DiVA, id: diva2:1838429
Part of project
Symphony – Supply-and-Demand-based Service Exposure using Robust Distributed Concepts, Knowledge Foundation
Funder
Knowledge Foundation, 201901112024-02-162024-02-162024-04-24Bibliographically approved