Bibliographic record
Abstract
The extensive growth of the Internet of Things (IoT) has impacted diverse applications, including smart homes and cities, Intelligent Transport Systems (ITS) and smart factories. IoT integrates billions of smart devices -predicted to increase from 27 billion in 2017 to 125 billion by 2030- and manages communication between them. This degree of expanded connectivity requires extensive further analysis with respect to security, and the involvement of millions of factors and users increases vulnerability in IoT environments. However, Deep Learning (DL) approaches, which originated from machine learning (ML), have been efficient in many research fields, and current studies show the effectiveness of DL for IoT security applications. In this paper, we present detailed analyses of IoT security requirements and challenges, discuss the specific role of DL and review state-of-art research work in IoT environments using DL approaches. We also performed comparative analysis of DL algorithms such as RNN, LSTM, CNN, DBN and AE. And finally, we identified research issues in the current investigations, and outlined the future directions of DL algorithms in IoT security domains.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".