D1.3: COLLABS Innovations for Industrial IoT Systems1
Bibliographic record
Abstract
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. <br> 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".