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Found 2 papers, 2 papers with code
Automatic Malware Description via Attribute Tagging and Similarity Embedding
With the rapid proliferation and increased sophistication of malicious software (malware), detection methods no longer rely only on manually generated signatures but have also incorporated more general approaches like machine learning detection.
ALOHA: Auxiliary Loss Optimization for Hypothesis Augmentation
In this work, we fit deep neural networks to multiple additional targets derived from metadata in a threat intelligence feed for Portable Executable (PE) malware and benignware, including a multi-source malicious/benign loss, a count loss on multi-source detections, and a semantic malware attribute tag loss.
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