Deep Feature Extraction Framework Based on DNN for Enhancing Mirai Attachment Classification in Machine Learning
Bibliographic record
Abstract
Nowadays, the rapid growth of Internet of Things (IoT) devices has changed many parts of our lives by providing seamless connectivity and automation. However, this expansion has created new challenges and vulnerabilities, especially with regard to security. Mirai botnet, a strain of malware that has severely damaged the IoT ecosystem, is one of the prominent risks to IoT security. This research examines the impact of the Mirai botnet on IoT devices. In this study, we present a hybrid model to detect Mirai attacks. Deep neural networks (DNNs) are used to extract significant deep features, which are subsequently passed to the (Light Gradient Boosting Machine) LGBM algorithm for classification. A dataset from the Canadian Institute 2023 that included several kinds of Internet of Things assaults was used to assess the model. The model achieved excellent feature extraction and promising results with accuracy, recall, precision, and F1-score scoring up to 95 % for all. These accuracy results demonstrated the superior performance of the suggested model over competing techniques, which had scores of 71 %, 85%, and 52%, respectively, for Support Vector Machine (SVM), Light Gradient Boosting Machine (LGBM), and Stochastic Gradient Descent (SGD). Furthermore, the model outperforms the DNN model, which received a score of 65%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".