A distributed framework for zero-day malware detection using federated ensemble models.

ABSTRACT: Classification and detection of zero-day attacks remain a significant challenge within the domain of cybersecurity. Due to the vast types of malware families and the presence of an imbalanced dataset, real-time detection and classification become increasingly complex and inaccurate. Thus, there's an urgent need to develop an intelligent and adaptive defense mechanism capable of identifying and classifying such attacks with improved precision and robustness. This paper proposed a stacked ensemble federated learning model with an accuracy-aware node weighting scheme to address the challenges posed by inter- and intra-class similarities among different types of malwares. In the initial phase, malware Portable Executable (PE) files are collected from multiple online repositories and validated

SUBMITTER: Ishfaq H 

PROVIDER: S-EPMC12779155 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature