The Effectiveness of Deploying Machine Learning Techniques in Information Security to Detect Nine Attacks: UNSW-NB15 Dataset as a Case Study
Bibliographic record
Abstract
The expanding landscape of cyber threats, alongside the diminished effectiveness of traditional detection methods, has necessitated the exploration of machine learning (ML) techniques in information security.This study investigates the potential of various ML techniques in detecting a myriad of network threats using the UNSW-NB15 dataset, a comprehensive repository of diverse network attack instances.The dataset is initially analyzed and subsequently prepared for ML algorithms by transforming non-numerical attributes into numerical features using the popular "Label Encoder" encoding method.Subsequently, an array of ML techniques, including Decision Tree, Random Forest, Gradient Boosting, XGB, AdaBoost, MLP, and Voting, is deployed on the prepared dataset.Three experimental setups were designed: 1) Binary classification to distinguish between normal and malicious attack types.2) Multiclass classification to differentiate among various malicious attack types.3) An enhancement experiment to improve upon the second experimental setup.These experiments were conducted to evaluate the ability of each algorithm to discern among the malicious attack types represented in the UNSW-NB15 dataset.The results suggest that the voting classifier exhibited superior performance in the attack detection process.Furthermore, the XGB algorithm demonstrated higher evaluation metrics compared to other techniques.Consequently, the XGB algorithm outperformed others regarding the performance measures used in the detection process.This study offers valuable insights into the application of ML techniques in enhancing information security and detection efficacy of complex cyber threats.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".