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Recent researches have shown that machine learning based malware detection algorithms are very vulnerable under the attacks of adversarial examples.
Statistical theory of extreme values and some practical applications: a series of lectures, 1954
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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