ResilientEdge: an AI-Powered Edge Analytics Framework for Enhancing Cyber Security in Decentralized Power Systems Using Big Data Technologies
Bibliographic record
Abstract
In an era of rapidly evolving cyber threats and increasing decentralization of power systems, traditional centralized cybersecurity frameworks are no longer sufficient. This paper proposes ResilientEdge, an AI-powered edge analytics framework designed to enhance cybersecurity in decentralized power systems using big data technologies. The proposed framework leverages edge computing to perform realtime threat detection and analytics close to the data source, significantly reducing latency and reliance on cloud infrastructures. Integrating federated learning and anomaly detection models, ResilientEdge ensures data privacy while enabling collaborative intelligence across distributed energy resources. Through a combination of architectural design, simulation analysis, and performance evaluation, this study demonstrates the framework's ability to outperform traditional centralized approaches, with notable improvements in detection accuracy (AUC=0.962), response time, and resilience. The findings highlight the framework's potential to revolutionize cybersecurity for critical infrastructure sectors such as power, energy, and transportation by merging AI, big data, and edge technologies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".