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Publication Detail
Prescience: Probabilistic Guidance on the Retraining Conundrum for Malware Detection
  • Publication Type:
  • Authors:
    Deo A, Dash SK, Suarez-Tangil G, Vovk V, Cavallaro L
  • Publication date:
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  • Notes:
    Acceptance Rate: 32%
Malware evolves perpetually and relies on increasingly sophisticatedattacks to supersede defense strategies. Datadrivenapproaches to malware detection run the risk of becomingrapidly antiquated. Keeping pace with malwarerequires models that are periodically enriched with freshknowledge, commonly known as retraining. In this work,we propose the use of Venn-Abers predictors for assessingthe quality of binary classification tasks as a first step towardsidentifying antiquated models. One of the key bene-fits behind the use of Venn-Abers predictors is that they areautomatically well calibrated and offer probabilistic guidanceon the identification of nonstationary populations ofmalware. Our framework is agnostic to the underlying classificationalgorithm and can then be used for building betterretraining strategies in the presence of concept drift. Resultsobtained over a timeline-based evaluation with about 90Ksamples show that our framework can identify when modelstend to become obsolete.
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