Enhancing IoT Security with Trust-Based Mechanism for Mitigating Black Hole Attacks
Bibliographic record
Abstract
This study focuses on enhancing both security and network performance in Mobile Ad Hoc Networks (MANETs) by integrating a trust model with the Ad hoc On-demand Multipath Distance Vector (AOMDV) routing protocol.The inherent structure of MANETs, characterized by dynamic, wireless connections between mobile nodes and a lack of centralized supervision, makes these networks particularly susceptible to security threats.Traditional security solutions designed for fixed networks prove inadequate for the unique challenges posed by ad hoc networks.We explore the benefits of multipath routing, which establishes multiple paths between source and destination nodes, thereby improving the reliability of data transmission and achieving load balancing.However, these benefits are undermined without a robust security framework.To this end, we introduce a trust model as a key mechanism for enhancing security.Trust is not only crucial for decision-making but also vital for the design and analysis of secure distribution systems.By assessing the trustworthiness of nodes, we aim to enhance both security and routing performance.Simulation results suggest that our proposed trust-based routing protocol is effective.Detailed findings will elucidate how this integrated approach can address the pervasive security challenges in MANETs while optimizing network performance.
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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 | 0.004 |
| 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 | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".