AMDDLmodel: Android smartphones malware detection using deep learning model

• Published in: PloS one Vol. 19; no. 1; p. e0296722
• Main Authors: Aamir, Muhammad, Iqbal, Muhammad Waseem, Nosheen, Mariam, Ashraf, M Usman, Shaf, Ahmad, Almarhabi, Khalid Ali, Alghamdi, Ahmed Mohammed, Bahaddad, Adel A
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 19-01-2024; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Analysis; Artificial neural networks; Autopsies; Behavior; Classification; Computer viruses; Computers, Handheld; Control; Datasets; Deep Learning; Efficiency; Engineering; Feature selection; Identification and classification; Machine learning; Malware; Mental Recall; Mobile applications; Neural networks; Performance evaluation; Privacy; Safety and security measures; Smart phones; Smartphone; Smartphones; Social aspects; Spyware; Support vector machines; Saudi Arabia
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0296722

Android is the most popular operating system of the latest mobile smart devices. With this operating system, many Android applications have been developed and become an essential part of our daily lives. Unfortunately, different kinds of Android malware have also been generated with these applications' endless stream and somehow installed during the API calls, permission granted and extra packages installation and badly affected the system security rules to harm the system. Therefore, it is compulsory to detect and classify the android malware to save the user's privacy to avoid maximum damages. Many research has already been developed on the different techniques related to android malware detection and classification. In this work, we present AMDDLmodel a deep learning technique that consists of a convolutional neural network. This model works based on different parameters, filter sizes, number of epochs, learning rates, and layers to detect and classify the android malware. The Drebin dataset consisting of 215 features was used for this model evaluation. The model shows an accuracy value of 99.92%. The other statistical values are precision, recall, and F1-score. AMDDLmodel introduces innovative deep learning for Android malware detection, enhancing accuracy and practical user security through inventive feature engineering and comprehensive performance evaluation. The AMDDLmodel shows the highest accuracy values as compared to the existing techniques.