Ensemble of Tree Classifiers for Improved DDoS Attack Detection in the Internet of Things
Bibliographic record
Abstract
IoT networks are made up of devices that have few computer resources, like less battery life, less processing power, less memory, and most importantly, minimum security, defense mechanisms, and infrastructure to protect them.As the number of IoT devices are growing fast, we can't ignore the impact of large-scale DDoS attacks that come from IoT devices.Artificial intelligence, including Deep Learning and machine learning (ML), is critical in the categorization and detection of DDoS attacks in the Internet of Things.We hope to contribute to current research by enhancing the efficiency with which Intrusion Detection Systems (IDS) identify DDoS attacks.This research paper focuses on exploring the effectiveness of tree-based classifiers and ensemble classifiers.The ensemble approaches used are voting and Stacking with a particular focus on Step Forward Feature Selection and average feature importance methods by performing evaluation to improve the classification of DDoS attacks, Decision Tree (DT), Extra Tree (ET), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) were selected as the best tree classifiers.Hyper-parameter tuning was performed to improve the performance of the classifiers.The proposed models are trained on Bot-IoT, CICIoT2023, and DS2OS datasets.The tree-based classifiers with ensemble of Stacking and voting demonstrated their capability to effectively detect and classify DDoS attacks.The result obtained from the proposed approach showcase an impressive accuracy rate of over 99%.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".