Innovative IoT security protocol: High-accuracy device identification and resilience against credential compromise (HADIRACC)
Bibliographic record
Abstract
The IoT ecosystem faces increasingly complex security challenges due to the rapid growth of global IoT devices. Security risks related to device identification and credential compromise are on the rise, especially with the proliferation of IoT devices in various aspects of life. This research highlights the need to address these vulnerabilities through the development of robust security protocols, aiming to create a more secure IoT ecosystem and enhance user trust in this technology. The objective of the research is the development of an innovative IoT security protocol; High-Accuracy Device Identification and Resilience Against Credential Compromise (HADIRACC). This paper contributes significantly to enhancing the security and reliability of the IoT ecosystem. The research methods employed encompass the development of security protocols, the development of a proximity-based solution, and the classification of IoT devices using data processing techniques and machine learning-based classification. This study involves the collection and pre-processing of datasets, training different classifiers using 70% of the dataset, and testing the classifiers using the remaining 30%. The proposed protocol can effectively enhance the security of IoT devices by addressing various scenario-based attacks. Furthermore, the results of the analysis of the five classifiers used in this study indicate that Random Forest has the highest F1 score accuracy, reaching 88.8%. This suggests that Random Forest, as a classifier, can make the most accurate predictions compared to other classifiers.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".