A Survey on Trust Models in Heterogeneous Networks
Bibliographic record
Abstract
Heterogeneous networks (HetNets) merge different types of networks into an integrated network system, which has become a hot research area in recent years towards next-generation communication networks. HetNets aim to effectively exploit network resources and provide seamless connectivity for heterogeneous objects. Unlike other networks, HetNets hold such characteristics as heterogeneity, openness, distribution, multi-domain involvement, thus are susceptible to various security threats and attacks. Traditional security approaches are not sufficiently effective in defending against them. With extensive study and practice, researchers found that trust models offer effective measures to enhance the security and reliability of a network system. However, there still lacks a comprehensive survey on the recent advances of trust models in HetNets. In this paper, we fill this gap. We first retrospect the history of HetNets research and introduce important concepts related to trust. Then, we propose a set of criteria that a sound trust model should satisfy, which can also serve as a measure to evaluate the quality of a trust model, i.e., Quality of Trust (QoT). We provide taxonomies of trust models and their applications, and continue with a thorough review on trust models in HetNets. Based on the review, a list of open issues is highlighted, and corresponding future research directions are suggested to advance the research on trustworthy HetNets.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".