TANF—Trustworthy Adaptive Neural Framework for Reliable and Scalable 6G Internet of Things
Bibliographic record
Abstract
The increasing complexity of 6G-IoT networks presents challenges in ensuring real-time trust assessment, computational efficiency, and security against adversarial threats. Existing frameworks struggle to dynamically adapt to evolving threats and high-volume data streams, leading to compromised decision reliability. This study proposes TANF (Trustworthy Adaptive Neural Framework), an advanced deep learning-driven trust evaluation system incorporating hierarchical processing, multi-domain trust layers, and Holo-Recursive Memory (HRM) for adaptive optimization. TANF prioritizes high-trust data streams using sensory stream balancing, dynamically allocates resources through task-specific synergy layers, and enhances memory recall by integrating past, present, and predictive state representations. The simulation, conducted in Edge-IIoTset, IoT-23 and CICIDS2017, evaluated trust assessment, computational latency, scalability, and adversarial detection. TANF achieves a precision of 92. 8%, a latency reduction of 34. 5% and a adversarial detection rate of 95. 6%, outperforming ERAI, ROBUST-6G, and IMCS.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".