Abstract
As introduced in Part II, FAME is built in a modular fashion to allow increased compatibility with further extensions.
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Notes
- 1.
We specify sequences or chains of transformations as vectors \(|\textbf{T}|\) = n represented as \((t_1, t_2, ..., t_n)\), without an upper boundary.
- 2.
We are aware that the classifiers available in aggregators often suffer from overparametrization, which can lead to higher classification rates at the expense of false positives [Vir21c]. Thus, we suggest retrieving labels from these platforms carefully, since this can yield different results compared to local implementations, depending on how sensitive the models are implemented.
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© 2023 The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature
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Labaca-Castro, R. (2023). Stochastic Method. In: Machine Learning under Malware Attack. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-40442-0_4
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DOI: https://doi.org/10.1007/978-3-658-40442-0_4
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