Interaction-Aware Trust Management Scheme for IoT Systems With Machine-Learning-Based Attack Detection
Bibliographic record
Abstract
The recent Internet of Things (IoT) adoption has revolutionized various applications while introducing significant security and privacy challenges. Traditional security solutions are unsuitable for IoT systems due to their dynamicity, heterogeneity, and resource constraints. Trust-based solutions are emerging as promising alternatives due to their ability to track the dynamic behavior in IoT systems. However, existing trust management schemes are implemented at the device level, raising several challenges, including device modification, that compromises certification and scalability, increased network overhead, and higher device resource utilization. To address these challenges, this article proposes a novel trust management scheme that shifts its implementation to a higher layer in the IoT system, specifically to the IoT access layer (e.g., gateway). The proposed scheme establishes trust based on typical device interactions with the gateway without requiring additional information from the device. It relies on objective attributes spanning communication, security, and advanced dimensions to compute the trust value of an IoT device. Additionally, an artificial neural network (ANN) is integrated to determine if the device acts maliciously or behaves normally. Simulation results demonstrate a notable improvement in the detection rate, primarily due to incorporating the proposed ANN, compared to the threshold-based approaches in the literature. Overall, the improvements highlight the significant advantage of the proposed scheme’s robustness.
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 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 | 0.001 | 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".