A Deep Learning Model for Dos/DDos Attack Detection in FOG Computing Assisted MQTT-IoT Environments
Bibliographic record
Abstract
The Deep Learning Model for Dos/DDoS Attack Detection in FOG Computing Assisted MQTT-IoT Environments (DLDADF) aims to enhance intrusion detection in IoT and IIoT networks. It leverages the Edge-IIoTset dataset, which incorporates seven architectural layers, including cloud, fog, and edge devices. It has various types of attacks, such as DoS/DDoS and infiltration. The DLDADF model features a mathematical model that defines trust-based metrics for secure data forwarding and a deep learning architecture to detect gunshots from audio signals through convolutional networks. A flexible and scalable strategy, guaranteeing resilient and secure data transmission across diverse IoT conditions. It holds significant potential for advanced improvements in security and adaptability against real-time threats and unstable network environments, thereby establishing a foundation for future research in federated deep learning conducted from a trust-oriented perspective. Using the following metrics, the DLDADF model has calculated training and validation of loss & accuracy, confusion matrix of the DLDADF model, detection accuracy, and ROC curve analysis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".