DDoS Detection in SD-IoT: A GA-Optimized Weighted Majority Vote Model Using SDN Simulated Datasets
Bibliographic record
Abstract
The convergence of the Internet of Things (IoT) and Software-Defined Networking (SDN) has given rise to Software-Defined IoT (SD-IoT) architectures that offer enhanced scalability, programmability, and centralized control. However, the constrained resources of IoT devices make these environments highly vulnerable to cyber threats, particularly Distributed Denial of Service (DDoS) attacks. In this work, we improve intrusion detection in SD-IoT environments through several key contributions. We built an SDN-based simulation environment to generate realistic network traffic, including five types of DDoS and botnet attacks. Using Iperf and Hping3, we produce both benign and malicious traffic, creating two balanced datasets: one with 140,000 records for binary classification and another with 60,000 records for multi-class classification. From 17 raw statistics, we derive 11 traffic features and apply Recursive Feature Elimination with Random Forest (RFE-RF) for the binary dataset and RFE with XGBoost (RFE-XGB) for the multi-class dataset to iteratively remove irrelevant or redundant features. Further, we develop an ensemble classification model composed of nine base classifiers, integrating their outputs through a weighted majority voting scheme. To optimize the influence of each classifier, we employ a Genetic Algorithm (GA) that evolves voting weights over generations based on classification accuracy. Our GA-optimized ensemble majority model achieves an F1-score of 98.76% for binary classification and 95.35% for multi-class detection.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| 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) | 0.000 | 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".