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.

翻译：高维特征表示被广泛应用于基于机器学习的网络攻击检测系统，但这类表示会增加计算复杂度，并可能阻碍其在资源受限环境中的部署。本文针对网络攻击分类任务，通过对比两种降维方法——主成分分析（PCA）与线性预测编码（LPC）——研究特征压缩技术。我们生成了不同维度的压缩特征表示，并在多种分类模型上进行了评估。实验分析表明，即使在强压缩条件下，PCA仍能保持分类性能；而LPC虽性能下降幅度略大，仍能提供具有竞争力的预测表示。研究结果表明，可在对分类精度影响极小的前提下实现特征维度的大幅缩减，这凸显了轻量级特征压缩技术在高效网络安全分析中的应用潜力。