Adaptive Routing and Security for Heterogeneous Networks Using Quantum Key Distribution and Bat Optimized Recurrent Neural Network
Why this work is in the frame
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Bibliographic record
Abstract
In contemporary heterogeneous networks, the reliance on robust and secure communication protocols is increasingly critical due to the rising sophistication of intruding techniques and diverse attack vectors. The dynamic nature of routing in these networks, coupled with nodes of varying computational capabilities, poses a risk of routing attacks, which significantly compromise network security and performance. To address these challenges, this paper introduces an advanced framework combining Post-Quantum Cryptography (PQC) with Bat Optimization Algorithm (BOA) based Adaptive Quantum Routing RNN (AQR-RNN) to enhance security and routing efficiency. Quantum Key Distribution (QKD) is employed to secure communications, thus providing a robust defense against threats. Simultaneously, BOA- AQR-RNN is utilized to optimize routing efficiency, inspired by the echolocation capabilities of bats. This approach leverages AQR-RNN architectures to adaptively learn and predict routing paths, enhancing decision-making and optimization processes. The synergy between QKD and BOA- AQR-RNN approach not only strengthens the security framework of heterogeneous network routing protocols but also achieves superior Quality of Service (QoS) by dynamically optimizing routing strategies. The proposed methodology demonstrates significant potential for advancing secured communication in Internet of Things (IoT) environments and other complex network architectures.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it