Dimensionality Reduction for Cyberattack Classification: A Comparative Evaluation of PCA and Linear Predictive Coding

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Advanced, long

Summary

A comparative study evaluated Principal Component Analysis (PCA) and Linear Predictive Coding (LPC) as feature compression techniques for lightweight cyberattack classification. Researchers used the CICIDS2017 dataset, comprising 2,574,264 samples with 78 numerical features across 15 attack classes. The study generated compressed feature representations of four, eight, and 12 dimensions, assessing their impact on various machine learning classifiers. Experimental results showed that PCA effectively preserved classification performance, even with aggressive compression, such as PCA-4 reducing dimensionality by approximately 94.9% with minimal accuracy loss. LPC also provided competitive predictive representations, though with slightly larger performance degradation. Ensemble tree-based classifiers, particularly Random Forest, consistently achieved the strongest performance and demonstrated robustness to feature compression, while also reducing computational costs for both training and inference.

Key takeaway

For Machine Learning Engineers deploying cyberattack detection systems in resource-constrained environments, aggressively reducing feature dimensionality is highly effective. You should prioritize Principal Component Analysis (PCA) for feature compression, as it maintains near-baseline classification accuracy even with a 94.9% reduction. Consider using ensemble tree-based models like Random Forest, which prove robust with compressed features, significantly lowering computational costs for training and inference.

Key insights

Aggressive feature compression via PCA or LPC maintains high cyberattack classification accuracy, enabling efficient deployment.

Principles

• PCA preserves classification performance under aggressive compression.
• Ensemble tree-based classifiers are robust to feature compression.
• Predictive coding can create compact, informative representations.

Method

The study preprocesses cybersecurity data, applies PCA or LPC for dimensionality reduction (4, 8, 12 dimensions), then trains and evaluates multiple ML classifiers (Logistic Regression, SVM, Random Forest, Gradient Boosting, MLP) on the compressed features.

In practice

• Use PCA for ~95% feature reduction with minimal accuracy loss.
• Prioritize Random Forest for robust performance with compressed features.
• Apply LPC for compact predictive representations in tabular data.

Topics

• Dimensionality Reduction
• Principal Component Analysis
• Linear Predictive Coding
• Cyberattack Classification
• Machine Learning
• Resource-Constrained Systems
• CICIDS2017 Dataset

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.