文章基本信息

标题：Detection of Android Malware App through Feature Extraction and Classification of Android Image
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作者：Mohd Abdul Rahim Khan ; Nand Kumar ; R C Tripathi 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2022
卷号：13
期号：5
DOI：10.14569/IJACSA.2022.01305103
语种：English
出版社：Science and Information Society (SAI)
摘要：Android apps have security risks due to rapid development in android devices. In the Android ecosystem, there are many challenges to detecting Android malware. Traditional techniques such as static, dynamic, and hybrid approach, most of the existing approaches require a high rate of human intervention to detect Android malware. Most of the current techniques have the most significant security challenges to detect Android malware, the inspection of Android Package Kit(APK) file structures, increased complexity, high processing power, more storage space, and much human intervention. This paper proposed Machine Learning(ML)based algorithms to detect Android malware apps through feature extraction and classification of grayscale images. In our proposed approach, convert most of the files of APK such multiDex, resources, certificate, and manifest files transform into a grayscale image, using the image algorithm to extract the local feature of the image. In the paper used different ML models to classify the local features with the help of multiple images of malware families. This approach deals with the obfuscation attack.it can hide in any files of APK. The proposed approach enhanced accuracy reached up to 96.86%, and computation time did not increase more than the existing techniques. The quality of that proposed worked; it has a high classification accuracy and less complexity validation loss.
关键词：Android malware; obfuscation attack machine learning; android application package (APK); android malware app; grayscale images