Dimensionality Reduction for Cyberattack Classification: A Comparative Evaluation of PCA and Linear Predictive Coding
arXiv:2606.05584v1 Announce Type: cross 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
The increasing sophistication of cyberattacks and the expanding attack surface necessitate more efficient and robust detection systems, driving innovation in underlying AI techniques.
This research directly addresses the practical deployment challenges of AI-driven cybersecurity in diverse environments, improving the efficacy and accessibility of defense mechanisms.
The ability to deploy advanced cyberattack detection systems more efficiently in resource-constrained environments or at scale through optimized feature representations.
- · Cybersecurity providers
- · Organizations with limited compute resources
- · Edge AI developers
- · Defense contractors
- · Sophisticated cyber attackers
- · Legacy signature-based detection systems
Improved real-time cyberattack detection and faster response times due to more efficient computational models.
Reduced operational costs for deploying and maintaining AI-driven cybersecurity solutions in distributed networks.
A potential shift towards more pervasive and accessible AI-powered defense mechanisms, altering the cost-benefit analysis for cyber offensive operations.
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Read at arXiv cs.AI