DTEXNet: Artificial Intelligence-Based Combination Scheme for DDoS Attacks Detection
Bibliographic record
Abstract
Distributed Denial of Service (DDoS) attacks became the most widely spread attack because of their ease of design and execution. Such attacks are challenging to detect and mitigate due to the diversity of DDoS attack modes and the variable size of attack traffic. This makes the research on DDoS attack detection extremely important. Machine learning techniques are used to detect various DDoS attacks with complex and dynamic patterns. However, such techniques require extensive pre-processing and feature engineering to the data to achieve acceptable results. On the other hand, neural networks can achieve acceptable results without the need for such prior preparations. This paper proposes a novel combination scheme between EfficientNet, Xception, and Decision Tree models called DTEXNet. DTEXNet combines two neural networks to benefit from their ability to extract features without the need to prior preparations, and a classical machine learning model that has high performance on similar problems. The solution proposed uses two convolutional neural networks (CNNs) to classify between 10 types of DDoS attacks and uses their prediction results to enhance the performance of Decision Tree model on the same classification task. The results of the experiments carried out show that the proposed solution can significantly improve the results of the Decision tree, EfficientNet, and Xception models if applied individually.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".