Machine Learning DDOS Attack Detection Based-On Transfer Learning with Multi-Layer Perceptron
Bibliographic record
Abstract
A DDoS attack is a malicious effort to interrupt the regular flow of traffic to a specific server, service, or network by overwhelming the target or its surrounding infrastructure with a flood of Internet traffic. It employs client/server technology to combine several systems into an attack platform for launching assaults on one or more targets, thus enhancing the attack's efficiency. Lately, the most utilized algorithms for identifying DDoS attacks are grounded in Machine Learning (ML) and Deep Learning (DL). The research presented in this paper aims to provide results from applying transfer learning on a pretrained MLP model. The MLP model was initially trained on CIC-DDoS2019 dataset for establishing the base MLP model and after that using NSL-KDD dataset for conducting transfer learning. The goal of training a robust MLP model and subsequently fine-tuning it with another dataset is to show that this can enhance performance by transferring knowledge from one dataset to another through deep feature extraction and understanding, without needing to conduct deep feature engineering prior to transfer learning. According to the findings of the study, it was demonstrated that transfer learning attained high accuracy for both training and testing at 93%, all without conducting feature selection on dataset 2 (NSL-KDD).
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".