AutoDP-LLM: Automating Data Pre-processing for Intrusion Detection Systems using Large Language Models

Excerpt

arXiv:2610.05369v1 Announce Type: cross Abstract: The increasing complexity and scale of modern cyber-attacks demand intelligent and computationally efficient Intrusion Detection Systems (IDS). However, designing effective data pre-processing pipelines traditionally involves substantial trial-and-error effort and repeated evaluation of alternative configurations. For large, high-dimensional network traffic data, this process can create a significant computational burden. In this work, we propose