LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection

This research investigates using large language models (LLMs) for zero-shot feature selection in malware detection as an alternative to traditional statistical methods. The study evaluates multiple LLMs (GPT-5.0, GPT-4.0, Gemini-2.5) on the EMBOD dataset against conventional feature selection methods across various classifiers. Results show that LLM-guided zero-shot feature selection achieves competitive performance with traditional methods while providing enhanced interpretability, stability, and reduced dependence on labeled data.