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

arXiv: https://arxiv.org/abs/2606.05584 · HTML: https://mlsanity.com/paper/2606.05584/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 31 references that are in the map.
- First posted on arXiv 4 Jun 2026.

## Builds on (most cited first, 8 of 8)

- [A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security](https://mlsanity.com/paper/1807.11023/) (2018)
- [Machine Learning in IoT Security: Current Solutions and Future Challenges](https://mlsanity.com/paper/1904.05735/) (2019)
- [Reinforcement Learning for IoT Security: A Comprehensive Survey](https://mlsanity.com/paper/2102.07247/) (2021)
- [Cybersecurity and Frequent Cyber Attacks on IoT Devices in Healthcare: Issues and Solutions](https://mlsanity.com/paper/2501.11250/) (2025)
- [IoT Botnet Detection Using an Economic Deep Learning Model](https://mlsanity.com/paper/2302.02013/) (2023)
- [A Review on the Security Vulnerabilities of the IoMT against Malware Attacks and DDoS](https://mlsanity.com/paper/2501.07703/) (2025)
- [CryptoDNA: A Machine Learning Paradigm for DDoS Detection in Healthcare IoT, Inspired by crypto jacking prevention Models](https://mlsanity.com/paper/2501.18549/) (2025)
- [Extreme Learning Machine Based System for DDoS Attacks Detections on IoMT Devices](https://mlsanity.com/paper/2507.05132/) (2025)

## Abstract (arXiv)

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.