A Firefly-Based Optimization Algorithm for Secure 5G-IoT Cyber-Physical Systems
Bibliographic record
Abstract
The interaction between cyber-physical systems (CPS) and 5G-enabled Internet of Things (IoT) networks introduce critical challenges related to security, resource efficiency, and simulated data threat detection.Existing security mechanisms struggle to adapt to the dynamic and heterogeneous nature of these network infrastructures.To address these challenges, this study proposes a Firefly Optimization Algorithm (FOA) inspired by swarmbased firefly intelligence to enhance security, resource allocation, and energy efficiency in CPS-IoT networks.The proposed approach integrates an enhanced attraction-based motion mechanism and an adaptive mutation strategy to dynamically adjust security parameters, optimizing intrusion detection, anomaly mitigation, and encryption complexity.Empirical evaluations demonstrate that FOA outperforms existing methods in terms of detection accuracy, latency reduction, and computational efficiency, ensuring a robust and adaptive security framework for next-generation CPS systems.This research contributes to the development of intelligent, adaptive, and sustainable security solutions for 5G-enabled IoT ecosystems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".