LSTM-based Approach for Android Malware Detection

Abstract

The rise of Android malware has become a growing concern for individuals, institutions, and governments around the world. As the popularity of Android devices grows continuously, cybercriminals are finding new ways to exploit vulnerabilities in the Android operating system to distribute malware, steal data, and launch attacks. This research paper explores the present state of Android malware, including the types of malware that are most prevalent, the techniques used by cybercriminals to distribute malware, and the impact of Android malware on individuals and organizations. The paper also examines the strategies that can be used to protect Android devices from malware and the legal and ethical implications of using malware to monitor or control Android devices. The findings of this paper have important implications for individuals, organizations, and policymakers, and can help inform the development of more effective strategies for protecting against Android malware.In this work, the Long Short-Term Memory (LSTM) approach is proposed to detect Android malware. To validate our proposed approach, we have computed performance metrics including accuracy, precision, recall, and F1-Score. Additionally, we conducted a comparison with existing work.