Optimized Ensemble Model with Genetic Algorithm for DDoS Attack Detection in IoT Networks
Bibliographic record
Abstract
The growth in Internet of Things (IoT) networks has made them more vulnerable to various cyber threats, in-cluding Distributed Denial of Service (DDoS) attacks. Addressing DDoS attacks in resource-constrained IoT environments demands advanced detection methods beyond traditional cybersecurity. Existing machine learning and deep learning models have a tradeoff between accuracy and complexity. Pruning and quan-tization techniques present challenges related to precision and customization, highlighting the need for more balanced solutions. In response to these challenges, this paper introduces a novel Optimized Ensemble Model with Genetic Algorithm (OMEGA) system designed to detect high- and low-volume DDoS attacks in resource-constrained IoT networks. The system employs a combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) networks in its ensemble model to detect these attacks. In addition, the system employs novel post-training GA-based pruning and Min-Max quantization techniques for optimization. This combination enhances the detection accu-racy of high- and low-volume DDoS and significantly reduces computational demands, making the OMEGA system suitable for deployment in edge devices with limited resources. The OMEGA system is tested using a real-world IoT testbed and various datasets, showing an accuracy of over 90%.
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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 | 0.000 |
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".