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In this study deep learning methods, which have been tried to be used successfully in all areas of life in recent years, are tested in mobile malware detection.
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Malware detection and classification is a challenging problem and an active area of research. Particular challenges include how to best treat and preprocess ...
Dec 29, 2020 · Bibliographic details on Combat mobile malware via N-gram based deep learning.
Jul 8, 2018 · Combat Mobile Malware via N-gram Based Deep. Learning. Burak DÜSÜN ... “Droid-Sec: deep learning in android malware detection" in SIGCOMM.
▫ Training (N-gram with n is a customizable length parameter e.g. n = 2). ▫ Labeling (malicious, benign) based on ground truths. (from existing malware ...
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Jul 20, 2016 · I was reading a paper on the use of n-grams to detect malware. Could anyone provide a brief overview of how it works, just so I can confirm ...
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This study aimed to create a malware detection model based on a natural language model called skip-gram to detect evasive malware with the highest accuracy ...
Apr 23, 2021 · In this paper, we present an N-gram, semantic-based neural modeling method to detect the network traffic generated by the mobile malware.
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Sep 14, 2024 · This paper analyzes in detail several classical methods of feature extraction in malware detection techniques.
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R. Can Aygun's 5 research works with 189 citations, including: Combat Mobile Evasive Malware via Skip-Gram-Based Malware Detection.
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