Dimensionality Reduction for Cyberattack Classification: A Comparative Evaluation of PCA and Linear Predictive Coding
For cybersecurity practitioners deploying ML-based detection in resource-constrained environments, this work evaluates lightweight feature compression techniques to reduce computational overhead.
The paper compares PCA and LPC for dimensionality reduction in cyberattack classification, finding that PCA preserves performance under aggressive compression while LPC shows slightly larger degradation, enabling substantial feature reduction with minimal accuracy loss.
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.