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- W4313495843 abstract "This article presents a hybrid method that statically evaluates several aspects regarding network-based model, text, as well as permissions, and monitors several activities dynamically; those activities include system call logs, memory usage, as well as the CPU usage. Thus, the designed machine learning-based model can easily distinguished, as well as identify Android ransomware among different and varied types of malware. According to the results, the machine learning-based approach proposed for Android ransomware classification and detection is able to classify unknown malware that exhibits the same static and dynamic behavior; it can prevent adversarial evasion attacks. Moreover, it was found that the data from permissions and system calls are more valuable to detect ransomware. These features and other considered static and dynamic features detect and classify Android Ransomware with high accuracy." @default.
- W4313495843 created "2023-01-06" @default.
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- W4313495843 date "2022-11-29" @default.
- W4313495843 modified "2023-09-26" @default.
- W4313495843 title "Adversarial Ensemble Modeling for Evasion Attack Detection" @default.
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- W4313495843 doi "https://doi.org/10.1109/scisisis55246.2022.10002083" @default.
- W4313495843 hasPublicationYear "2022" @default.
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