Designing a Neural Network and a Genetic- Algorithm-Based Adaptive Wavelet for Internet Traffic Containing DDoS Attacks
Bibliographic record
Abstract
This paper presents the design of an adaptive mother wavelet for detecting Internet traffic data (ITD) with distributed denial of service (DDoS) attacks (DDoS ITD). The proposed procedure consists of designing an adaptive mother wavelet genetic neural network (GNN) for detecting the DDoS ITD, A multi-objective optimization based on a genetic algorithm is used to create a set of adaptive mother wavelets that best fit the weight parameters for a given input data recording. Moreover, a weighted cost function is used to measure how well the GNN is able to create a mother wavelet. The best mother wavelet coefficients for detecting DDoS attacks are achieved with coefficients [−0.3744,0.0034]. The created mother wavelet increased the detection rate of the DDoS attacks by 0.3% when compared to the Haar mother wavelet.
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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 | 0.000 |
| 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".