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

2026-06-03

AI Summary

This study addresses the computational burden imposed by high-dimensional features in network intrusion detection, which hinders deployment in resource-constrained environments. It presents the first systematic comparison of Principal Component Analysis (PCA) and Linear Predictive Coding (LPC) in terms of dimensionality reduction efficacy and performance preservation for attack classification tasks, proposing a lightweight feature compression strategy. Experimental results demonstrate that PCA maintains high classification accuracy even under aggressive compression, while LPC also exhibits competitive predictive performance. Both methods achieve substantial dimensionality reduction with minimal impact on model accuracy, thereby validating the feasibility and effectiveness of lightweight feature representations in network intrusion detection systems.

Abstract

High-dimensional feature representations are widely used in machine learning-based cyberattack detection systems. However, they increase computational complexity and may hinder deployment in resource-constrained environments. In this paper, we investigate feature compression techniques for cyberattack classification by comparing two dimensionality reduction approaches: Principal Component Analysis (PCA) and Linear Predictive Coding (LPC). Compressed feature representations with varying dimensionalities are generated and evaluated across several classification models. Experimental analysis demonstrates that PCA preserves classification performance even under aggressive compression. On the other hand, LPC provides competitive predictive representations with slightly larger performance degradation. The results show that substantial reductions in feature dimensionality can be achieved with minimal impact on classification accuracy, highlighting the potential of lightweight feature compression for efficient cybersecurity analytics.

Problem

• Dimensionality Reduction
• Cyberattack Classification
• Feature Compression
• Computational Complexity
• Resource-Constrained Environments

Innovation

• Dimensionality Reduction
• Cyberattack Classification
• Principal Component Analysis
• Linear Predictive Coding
• Feature Compression