TY - JOUR ID - 88133 TI - Android Malware Category and Family Identification Using Parallel Machine Learning JO - Journal of Information Technology Management JA - JITM LA - en SN - AU - Hashem El Fiky, Ahmed AU - Madkour, Mohamed Ashraf AU - El Shenawy, Ayman AD - M.Sc. in Systems and Computers Engineering, Department of Systems and Computers Engineering, Faculty of Engineering Al-Azhar University, Cairo, Egypt. AD - Professor, Department of Systems and Computers Engineering, Faculty of Engineering Al-Azhar University, Cairo, Egypt. AD - Assistant Professor, Department of Systems and Computers Engineering, Faculty of Engineering Al-Azhar University, Cairo, Egypt; Software Engineering and Information Technology, Faculty of Engineering and technology, Egyptian Chinese University, Cairo, Egypt. Y1 - 2022 PY - 2022 VL - 14 IS - 4 SP - 19 EP - 39 KW - Android Malware KW - Malware Analysis KW - Malware Category Classification KW - Malware Family Classification KW - Malware Dynamic Analysis DO - 10.22059/jitm.2022.88133 N2 - Android malware is one of the most dangerous threats on the Internet.  It has been on the rise for several years.  As a result, it has impacted many applications such as healthcare, banking, transportation, government, e-commerce, etc.  One of the most growing attacks is on Android systems due to its use in many devices worldwide.  De-spite significant efforts in detecting and classifying Android malware, there is still a long way to improve the detection process and the classification performance.  There is a necessity to provide a basic understanding of the behavior displayed by the most common Android malware categories and families.  Hence, understand the distinct ob-jective of malware after identifying their family and category.  This paper proposes an effective systematic and functional parallel machine-learning model for the dynamic detection of Android malware categories and families.  Standard machine learning classifiers are implemented to analyze a massive malware dataset with 14 major mal-ware categories and 180 prominent malware families of the CCCS-CIC-AndMal2020 on dynamic layers to detect Android malware categories and families.  The paper ex-periments with many machine learning algorithms and compares the proposed model with the most recent related work.  The results indicate more than 96 % accuracy for Android Malware Category detection and more than 99% for Android Malware family detection overperforming the current related methods.  The proposed model offers a highly accurate method for dynamic analysis of Android malware that cuts down the time required to analyze smartphone malware. UR - https://jitm.ut.ac.ir/article_88133.html L1 - https://jitm.ut.ac.ir/article_88133_16d42429ea8c150b3d16ef50fe0a21d7.pdf ER -