Evaluation of UDP-Based DDoS Attack Detection by Neural Network Classifier with Convex Optimization and Activation Functions
Bibliographic record
Abstract
Distributed Denial of Service (DDoS) stands as a critical cybersecurity concern, representing a malicious tactic employed by hackers to disrupt online services, network resources, or host systems, rendering them inaccessible to legitimate users.DDoS attack detection is essential as it has a wide-ranging impact on the field of computer science.This is quantitative research to evaluate Multilayer Perceptron (MLP) classification algorithm with different optimization methods and different activation functions on UDP-based DDoS attack detection.The CIC-DDoS2019 DDoS evaluation dataset, known for its inclusion of modern DDoS attack types, was instrumental in this study by the Canadian Institute for Cyber Security.The CIC-DDoS2019 dataset encompasses eleven DDoS attack datasets, which are UDP, UDP-Lag, NTP, and TFTP datasets were utilized in this investigation.This study proposes a novel feature selection approach.It specifically targets datasets related to UDP-based DDoS attacks.The approach aims to identify groups of features that share the uncorrelated characteristic.It means None of the features within a subset have a significant relationship with each other as measured by three correlation methods: Pearson, Spearman, and Kendall.To further validate the proposed approach, the researchers conducted experiments on a specially crafted DDoS attack dataset.MLP classification algorithm along with ADAM optimization method and Tanh activation function produce the better results for UDP-based DDoS attack detection.This combination produces the better accuracy values of 99.97 for UDP Flood attack, 99.77 for UDP-Lag attack, 99.70 for NTP attack, 99.93 for TFTP attack and 99.76 for UDP customized DDoS attack.
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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 | 0.001 | 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 | 0.006 |
| 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".