D1.3: COLLABS Innovations for Industrial IoT Systems1
Authors/Creators
- 1. University of Novi Sad Faculty of Sciences
- 2. THALES SIX GTS FRANCE SAS
- 3. INFORMATION TECHNOLOGY FOR MARKET LEADERSHIP
- 4. RENAULT SAS
- 5. INFINEON TECHNOLOGIES AG
- 6. PHILIPS CONSUMER LIFESTYLE BV
- 7. UNIVERSITA DEGLI STUDI DI PADOVA
- 8. ADVANCED LABORATORY ON EMBEDDED SYSTEMS SRL
- 9. SPHYNX TECHNOLOGY SOLUTIONS AG
- 10. SIEMENS AKTIENGESELLSCHAFT
- 11. HAROKOPIO UNIVERSITY
Description
In this deliverable we briefly summarize significant new research results and new commercial products that have been published or released on the market, since the submission of the project proposal in 28/03/2019, in the technological areas where COLLABS aims to advance the state-of-the-art (SotA thereafter). The goal of this document is to ensure that the starting point for these targeted advances is up to date. It thus contains one section for each technological area listed as a target for advance in Section 1.4.1. of the proposal which describes the project’s offerings beyond the SotA.
The deliverable is organized as follows. The introductory Section 1 briefly describes the overall setting and the main problem addressed by COLLABS, its project statement and the structure of the proposed COLLABS framework, in order to provide context for the sections that follow. Sections 2–8 respectively focus on the SotA in areas of: data protection (Section 2), distributed anomaly detection and remote attestation (Section 3), physical security (Section 4), function as a service (FaaS, Section 5), secure and trusted execution environments (Section 6), encrypted traffic analysis (Section 7), and AI-enabled cybersecurity in IIoT-based collaborative manufacturing environments (Section 8). Section 9 concludes the deliverable with a summary of the impacts of the new SotA on project implementation and the outlook of its contribution.
During the past year there have been many works in the domains relevant to COLLABS, notably in blockchain applications, machine- and deep-learning based anomaly detection and wireless system design, remote attestation for tackling security issues in IoT systems, implementations of secure multiparty computation (SMC) and homomorphic encryption (HE), platforms providing trusted execution environments (TEEs), encrypted network traffic analysis for various purposes, and (distributed) machine learning in different levels of cybersecurity for IIoT systems. These works, overviewed in this document, provide a strong foundation for COLLABS project implementation without jeopardizing its goals with relation to SotA advancements.
Files
D1.1 COLLABS Innovations for Industrial IoT Systems.pdf
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(1.6 MB)
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