Apr 20, 2020 · Our proposed model decompiles an application and extracts opcode sequences. Then, it uses a state-of-the-art NLP word embedding algorithm, which ...
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 20, 2020 · Our proposed model starts with extracting skip-gram-based features from instruction sequences of Android applications. Then it applies several ...
Combat Mobile Evasive Malware via Skip-Gram-Based Malware Detection. Authors ... skip-gram to detect evasive malware with the highest accuracy rate possible.
Apr 20, 2020 · Our proposed model starts with extracting skip-gram-based features from instruction sequences of Android applications. Then it applies several ...
In this study, we aimed to create a malware detection model based on a natural language model called skip-gram to detect evasive malware with the highest ...
Combat Mobile Evasive Malware via Skip-Gram-Based Malware Detection · Alper ... DL-Droid: Deep learning based android malware detection using real devices.
People also ask
How to detect malware on Android?
Which technique is used for analyzing malware without executing it?
How do existing malware protection solutions work?
Jul 1, 2024 · This paper proposes signature-based malware detection using permission and broadcast-receiver data, which is extracted from the manifest file.
Missing: Combat Gram-
May 8, 2021 · Combat Mobile Evasive Malware via Skip-Gram-. Based Malware Detection[9]. Deep Learning based and machine learning. RF - 95.64% on entire ...
Jun 3, 2024 · Combat Mobile Evasive Malware via Skip-Gram-Based Malware Detection. ... Combat mobile malware via N-gram based deep learning. SIU 2018: 1 ...