Evaluation of Naive Bayes, Random Forest and Stochastic Gradient Boosting Algorithm on DDoS Attack Detection
Bibliographic record
Abstract
The Internet has given unlimited access to every user through the network used. Vulnerabilities in a network can also be caused by increasing knowledge about hacking and cracking. This is the reason why information and network security are so important. The dataset used in this study uses a dataset from CIC (Canadian Institute Cybersecurity), which covers 7 different attack scenarios, including brute-force, heartbleed, botnet, dos, DDoS, web attacks, and network infiltration from within. The existing attack documents will be extracted. Feature extraction is a process to find the feature values contained in documents for the text mining process. Based on this explanation, this DDoS attack will generate a log where the attack log will be processed and processed into a CSV file for the classification process using Naive Bayes, Random Forest, and Stochastic Gradient Boosting. In this study, researchers used the Naive Bayes, Random Forest, and Stochastic Gradient Boosting algorithms to generate a classification comparison of DDoS attack data so that researchers can find out which algorithm is the best in generating classifications for DDoS attack cases. The results of this study can be concluded that the average accuracy generated by Naive Bayes is 82.45%, the average accuracy generated by the Random Forest algorithm is 99.78%, and the average accuracy generated by the Stochastic Gradient Boosting algorithm is 100%, so that the SGB algorithm is better than Naive Bayes and Random Forest algorithms in classifying DDoS attacks.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".