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• W2000159976 abstract "We examine the problem of aggregating the results of multiple anti-virus (AV) vendors' detectors into a single authoritative ground-truth label for every binary. To do so, we adapt a well-known generative Bayesian model that postulates the existence of a hidden ground truth upon which the AV labels depend. We use training based on Expectation Maximization for this fully unsupervised technique. We evaluate our method using 279,327 distinct binaries from VirusTotal, each of which appeared for the first time between January 2012 and June 2014." @default.
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• W2000159976 date "2015-10-16" @default.
• W2000159976 modified "2023-10-16" @default.
• W2000159976 title "Better Malware Ground Truth" @default.
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• W2000159976 doi "https://doi.org/10.1145/2808769.2808780" @default.
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