A benchmark dataset with 3.2 million malicious and benign files across 6 file types for evaluating malware classifiers.
EMBER2024 is an updated malware dataset designed for researchers to explore a variety of classification tasks, including malicious/benign detection, malware family classification, and behavior prediction. It provides raw features and multiple label types for 3.2 million files, enabling holistic evaluation of machine learning models in cybersecurity.
EMBER2024 aims to provide a comprehensive, realistic benchmark that reflects the evolving malware landscape, enabling robust evaluation of classifier performance on novel and evasive threats.
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