Classification and Analysis of Android Malware Images Using Feature Fusion Technique
نویسندگان
چکیده
The super packed functionalities and artificial intelligence (AI)-powered applications have made the Android operating system a big player in market. smartphones become an integral part of life users are reliant on their smart devices for making calls, sending text messages, navigation, games, financial transactions to name few. This evolution smartphone community has opened new horizons malware developers. As variants growing at tremendous rate every year, there is urgent need combat against stealth techniques. paper proposes visualization machine learning-based framework classifying malware. from DREBIN dataset were converted into grayscale images. In first phase experiment, proposed transforms fifteen different image sections identifies files by exploiting handcrafted features associated with algorithms such as Gray Level Co-occurrence Matrix-based (GLCM), Global Image deScripTors (GIST), Local Binary Pattern (LBP) used extract sections. extracted further classified using learning like K-Nearest Neighbors, Support Vector Machines, Random Forests. second fused CNN form feature fusion strategy. classification performance was evaluated file section. results obtained Feature Fusion strategy compared results. experiment conclude fact that Fusion-SVM model most suited identification certificate Manifest (CR + AM) It attained high accuracy 93.24%.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2021
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2021.3090998