对比PCA与LPC在压缩网络攻击特征时的性能，发现两者都能大幅降维且保持高准确率。
Dimensionality Reduction for Cyberattack Classification: A Comparative Evaluation of PCA and Linear Predictive Coding
- 用PCA和LPC对攻击特征进行降维，比较其压缩效果。
- 即使压缩到原维度的10%，PCA仍保持分类准确率不变。
- 适合资源受限环境下的轻量化网络安全分析应用。
高维特征表示广泛用于基于机器学习的网络攻击检测系统，但会增加计算开销，不利于资源受限环境部署。本文通过对比主成分分析（PCA）和线性预测编码（LPC）两种降维方法，评估其在攻击分类中的表现。在不同维度下生成压缩特征，并在多个分类模型上测试。实验表明，即使在激进压缩条件下，PCA仍能保持分类性能；而LPC虽略有性能下降，但提供具有竞争力的预测表示。结果表明，可在几乎不影响分类准确率的前提下实现显著的特征维度缩减，凸显了轻量级特征压缩在高效网络安全分析中的潜力。
原文摘要 · Abstract (English)
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.
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