AutoDP-LLM: Automating Data Pre-processing for Intrusion Detection Systems using Large Language Models
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 AutoDP-LLM, an automated pre-processing framework designed to reduce manual pipeline development and computational overhead. Specifically, AutoDP-LLM leverages Large Language Models (LLMs) to autonomously generate and validate executable data pre...
| Authors | Bao-Phong Nguyen, Gia-Khanh Pham, Thai-Duong Do et al. |
|---|---|
| Primary category | cs.CR |
| Published | 4 October 2026 |
| Last updated at source | 4 October 2026 |
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