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- W3083609968 abstract "Detecting metamorphic malware provides a challenge to machine-learning models as trained models might not generalise to future mutant variants of the malware. To address this, we explore whether machine-learning models can be improved by augmenting training data-sets with samples of potential variants. These variants are generated using an evolutionary algorithm that evolves a behaviourally diverse set of mutants, optimised to avoid detection by a large set of existing detection-engines. Using features calculated from the behavioural trace of a sample as input, we evaluate the ability of five machine-learning methods to detect the new variants, show that the detection rate is considerably improved by including the new samples as training data, and that the classifiers still generalise over a range of malware. We then repeat this experiment using a sequence-based deep-learning method as the classifier, which is shown to out-perform the feature-based classifiers." @default.
- W3083609968 created "2020-09-11" @default.
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- W3083609968 date "2020-07-01" @default.
- W3083609968 modified "2023-09-27" @default.
- W3083609968 title "Improving Classification of Metamorphic Malware by Augmenting Training Data with a Diverse Set of Evolved Mutant Samples" @default.
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- W3083609968 doi "https://doi.org/10.1109/cec48606.2020.9185668" @default.
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