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Phantomware: a Data Augmentation Framework for the Detection of Emerging Malware Campaigns

arXiv cs.CRby Antonino Rullo ()1 min read2
Read at arxiv.org
AI summary · grok-4.7-buildSummaries by Vani AI

Phantomware augments scarce malware training data in latent space to detect unseen variants.

A cs.CR paper introduces Phantomware, a data-augmentation framework for detecting emerging malware when only a few samples exist. Unlike methods that generate executable variants, it works in the latent space of a pretrained detector and synthetically expands a target family's representations. The authors report improved detection of unseen variants and greater robustness against both real and adversarially generated samples.

Key facts
  • Functional malware augmentation is costly and hard to control.
  • Phantomware synthesizes family representations inside a pretrained detector.
  • Experiments improve detection of unseen and adversarial malware samples.
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Tags
malware-detectiondata-augmentationmachine-learningphantomwarelatent-spacearxiv

From the original article

1 min read at arxiv.org

Detecting unseen malware variants remains a major challenge as malware families continuously evolve. This issue is exacerbated in data-scarce scenarios such as emerging malware campaigns, where only a few samples are available to train robust detectors. A common strategy is to proactively generate malware variants and use them as augmentation data to expose models to a broader range of potential threats.

arXiv cs.CR

By Antonino Rullo · arxiv.org

Read the full article at arXiv cs.CR

A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.