Androhealthcheck: A malware detection system for android using machine learning

With the boom of malware, the area of malware detection and the use of gadget assist to gain knowledge in research drastically with the aid of researchers. The conventional methods of malware detection are incompetent to detect new and generic malware. In this article, a generic malware detection process is proposed using machine learning named AndroHealthCheck. The malware detection process is divided into four phases, namely android file collection, decompilation, feature mining and machine learning. The overall contributions made in AndroHealthCheck are as follows: (1) designing and implementing a crawler for automating the process of benign files download, (2) collection of unstructured data from the downloaded APK files through the decompilation process, (3) defining a proper mechanism for the feature selection process by performing a static analysis process, (4) designing and implementing a feature mining script for extracting the features from unstructured data collection from APK files, (5) generating a rich homemade data set for machine learning with a huge variety and different flavours of malware files from different families and (6) evaluating the performance of the generated data set by using different types of supervised machine learning classifiers. In this article, the overall architecture and deployment flow of AndroHealthCheck are also discussed. © The Author(s).