3 papers
cs.LG2026
LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection
Naveen Gill, Ajvad Haneef K, Madhu Kumar S D
Feature selection (FS) remains essential for building accurate and interpretable detection models, particularly in high-dimensional malware datasets. Conventional FS methods such a…
cs.CR2026
CAFE-GB: Scalable and Stable Feature Selection for Malware Detection via Chunk-wise Aggregated Gradient Boosting
Ajvad Haneef K, Karan Kuwar Singh, Madhu Kumar S D
High-dimensional malware datasets often exhibit feature redundancy, instability, and scalability limitations, which hinder the effectiveness and interpretability of machine learnin…
cs.CR2025
MeLeMaD: Adaptive Malware Detection via Chunk-wise Feature Selection and Meta-Learning
Ajvad Haneef K, Karan Kuwar Singh, Madhu Kumar S D
Confronting the substantial challenges of malware detection in cybersecurity necessitates solutions that are both robust and adaptable to the ever-evolving threat environment. The…