A Comparative Evaluation of Machine Learning Models and EDA through Tableau Using CICIDS2017 Dataset
Bibliographic record
Abstract
Machine learning is utilized globally in network security, but computers need time to learn. Machine learning can identify many hacker attacks that humans cannot. Business intelligence and machine learning are being studied to strengthen network systems. The research topic is briefly covered in the study. Academic articles on the study topic are examined, and effective research methods are described. In this work, Python is used as a medium to build popular algorithms and Tableau for visualizations. Machine learning models like AdaBoost, XGBoost, Random Forest, Decision Tree, KNearest Neighbor, and Linear Discriminant Analysis. The Canadian Institute for Cybersecurity’s CICIDS2017 dataset is used for in-depth analysis. Performance metrics for all the algorithms are computed, with the help of accuracy, F1-score, precision, and recall. The following investigation revealed that XGBoost is the better-performing algorithm. The random forest model is the best-performing model in terms of accuracy (98.38%), and F1-score (98.37%). Contrary to others, AdaBoost and linear discriminant analysis models have been proven to be less effective at preventing intrusions. On testing with different numbers of features in the random forest, it is discovered that the model with 35 features improves its performance.
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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 | 0.000 |
| 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".