Improving Intrusion Detection System Accuracy Through PCA-Based Feature Reduction and Machine Learning Techniques
Bibliographic record
Abstract
With the increasing number of devices connected to the internet and the expansion of the attack surface, a substantial amount of network traffic is generated on a daily basis. All of this traffic needs to be monitored and examined for possible threats and exploits. Intrusion Detection System (IDS) plays a critical role in the cybersecurity landscape, automatically detecting possible threats that may compromise the system. This paper explores the efficacy of various IDS methodologies, focusing on anomaly-based detection using machine learning techniques. This research uses the Canadian Institute for Cybersecurity Intrusion Detection System 2017 Dataset (CICIDS2017), which is a modern public imbalanced dataset that contains 14 types of attacks and employs Principal Component Analysis (PCA) for efficient feature selection to reduce the number of features from 78 to 33 while retaining a variance of 99%. The methodology revolves around balancing the dataset using the Synthetic Minority Oversampling Technique (SMOTE) applied exclusively to the training data. The research trained and compared two machine learning models, which are Support Vector Machine (SVM) and Random Forest (RF), in terms of accuracy, precision, and recall. The paper results reveal that the RF model outperforms the SVM model, achieving excellent results and confirming its suitability for high-accuracy intrusion detection tasks.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".