A Multi-Classifier for DDoS Attacks Using Stacking Ensemble Deep Neural Network
Bibliographic record
Abstract
DDoS (Distributed Denial of Service) attacks have emerged as a serious menace to the security and integrity of data and information systems. The primary aim of this attack is to take down the targeted system and prevent legitimate users from accessing its services. Identifying a DDoS attack is a challenging task, and it must be performed before initiating any countermeasure. DDoS attack detection has been effectively applied in many studies using Machine Learning (ML) and Deep Learning (DL). However, many existing models are unable to recognize the distinct and dynamic behavior of DDoS attacks because they employ datasets that were produced a long time ago and lack up-to-date attack scenarios, do not include packet-based bi-directional traffic flow, and do not contain complete network traffic. In addition, most studies carried out binary classification, however, there are many types of DDoS attacks, each with its unique characteristics. Classifying DDoS attacks can be useful when thwarting the attack and taking preventive measures. This paper presents a multi-classifier model using stacking ensemble deep neural networks that identify several types of DDoS attacks to address the issues mentioned above. Our proposed hybrid model incorporates Convolution Neural Network (CNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU), and we show that while evaluating models with large datasets such as CIC-DDoS2019, ensemble technique increases model performance. According to experimental results, our proposed model can reach an accuracy of 89.4%, which outperforms other similar methods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".