A Dual-Layer Trust Model based on Digital Twins for Underwater Acoustic Sensor Networks
Bibliographic record
Abstract
With the increasing deployment of Underwater Acoustic Sensor Networks (UASNs) in various marine applications, ensuring network security has become a significant concern. Trust models offer an adaptive security mechanism for such networks. This paper proposes a dual-layer trust model (DLtrust) based on digital twin (DT) architecture. First, a network framework is constructed using DTs, in which the Local DT employs an adaptive long short-term memory (LSTM) algorithm to detect conventional attacks, providing the primary defense mechanism. DLtrust further interacts with the Cloud DT in real time to obtain the optimal weighting of trust evidence, thereby accelerating model convergence. Additionally, a reinforcement learning scheme is integrated into the Cloud DT to optimize dynamic trust evaluation and refine the evidence aggregation strategy of the Local DT. By continuously perceiving the global network status and adjusting trust parameters, the Cloud DT effectively guides the Local DT to detect anomalous behavior, especially in cases of compromised models. To enhance resilience against Byzantine attacks, the model incorporates the multi-Krum algorithm, strengthening advanced defense capabilities. Simulation results demonstrate that DLtrust outperforms existing trust models in terms of average detection accuracy and error rate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".