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- W3085804706 abstract "The malware uses morphological and polymorphic methods to evade detection, traditional malware recognition methods have gradually failed to cope with large and variable malware. To overcome drawbacks of static or dynamic analysis techniques, we merge the static and dynamic features as a new feature vector and form a feature matrix. In order to handle the effects of feature interactions we build a model for the interaction between tow feature vector in an efficient and effective manner, and apply Factorization Machine (FM) as the final classifier for malware classification because it can handle the feature sparsity effectively. The experimental results show that the method has a high accuracy for malware classification and a low false negative rate for malicious and benign dataset." @default.
- W3085804706 created "2020-09-21" @default.
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- W3085804706 date "2020-01-01" @default.
- W3085804706 modified "2023-09-22" @default.
- W3085804706 title "Malware Classifications Based on Static-Dynamic Features and Factorization Machines" @default.
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- W3085804706 doi "https://doi.org/10.1007/978-981-15-8101-4_23" @default.
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