Detection of Android Malware App through Feature Extraction and Classification of Android Image

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.

@article{Khan2022,
  title     = {Detection of Android Malware App through Feature Extraction and Classification of Android Image},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {5},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Mohd Abdul Rahim Khan and Nand Kumar and R C Tripathi},
  doi       = {10.14569/IJACSA.2022.01305103},
  url       = {https://doi.org/10.14569/IJACSA.2022.01305103}
}