FUSE-IS: multi-modal data fusion for carbon-aware security in industrial energy systems
Bibliographic record
Abstract
Modern industrial energy systems are increasingly reliant on heterogeneous data streams from sensors, grid infrastructure, renewable forecasts, and cybersecurity telemetry. Effectively fusing these diverse sources is essential for achieving resilient, efficient, and sustainable operations. In this paper, we present a Fusion-based Unified Security and Energy efficiency approach for Industrial Systems (FUSE-IS), a novel multi-modal data fusion framework. FUSE-IS integrates deep learning-based threat detection, differential privacy mechanisms, and carbon-aware resource scheduling. It enhances security, privacy, and energy efficiency in industrial energy environments. Unlike traditional solutions that address these objectives in isolation, FUSE-IS employs a unified data fusion approach that combines these solutions. As a result, it enabled real-time adaptive decision-making for threat mitigation, data protection, and carbon-optimized computing. Experimental results demonstrate that FUSE-IS achieves 98.5% detection accuracy with only 1.2% false positives, while reducing energy consumption by 24% and carbon emissions by 20% compared to baseline methods. The framework maintains strong privacy guarantees ( ϵ = 0.9) with minimal accuracy degradation (0.7%). A case study on DDoS mitigation illustrates FUSE-IS’s ability to dynamically adjust defense strategies based on carbon intensity fluctuations, resulting in a 27% emission reduction during the attack window.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".