Deep Learning-Powered Multiclass Classification of DDoS Attacks on 6G-Connected IoT Devices
Bibliographic record
Abstract
The rapid expansion of the 6G network and the widespread deployment of IoT devices resulted in challenging the effective detection and mitigation of distributed denial of service (DDoS) attacks originating from Internet of Things (IoT) sources. This paper presents an approach to handle this difficulty using machine learning and deep learning models. The approach uses Convolutional Neural Network (CNN) and Random Forest classifiers for binary classification, and Artificial Neural Network (ANN) model for determining the precise type of attack among nine classes. Fisher's Score and Recursive Feature Elimination with Cross Validation (RFECV) feature selection techniques are employed in the proposed approach for increasing the effectiveness of the system. The proposed approach is validated on the Canadian Institute for Cybersecurity-2019 dataset and the model achieves an accuracy rate of 99.5% for binary classification and more than 90% for different class classification.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.001 |
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 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".