@article { author = {Hashem El Fiky, Ahmed and Madkour, Mohamed Ashraf and El Shenawy, Ayman}, title = {Android Malware Category and Family Identification Using Parallel Machine Learning}, journal = {Journal of Information Technology Management}, volume = {14}, number = {4}, pages = {19-39}, year = {2022}, publisher = {Faculty of Management, University of Tehran}, issn = {2980-7972}, eissn = {2980-7972}, doi = {10.22059/jitm.2022.88133}, abstract = {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.}, keywords = {Android Malware,Malware Analysis,Malware Category Classification,Malware Family Classification,Malware Dynamic Analysis}, url = {https://jitm.ut.ac.ir/article_88133.html}, eprint = {https://jitm.ut.ac.ir/article_88133_16d42429ea8c150b3d16ef50fe0a21d7.pdf} }