Authors - Ganesh Haricharan Mungara, Pranai Govind Soorneedi, Karthik Mungara, C.N.S.Vinoth Kumar Abstract - The proliferation of smartphones has transformed communication, work, and information access. However, this convenience has brought significant security challenges, particularly from malware that can compromise user data and privacy. Despite numerous antivirus applications, detecting and removing malware from Android devices remains a challenge. Current solutions of ten fail to detect sophisticated malware, necessitating the intervention of cyber security experts, which can compromise user privacy. This project aims to develop a tool that detects malware on Android devices based on installed applications, eliminating the need for users to install third-party software. The proposed solution leverages pattern matching by checking installed packages against a database of known malware. If a match is found, the tool indicates potential malware presence. This method offers a privacy-preserving approach, focusing on app behavior rather than relying solely on signatures, making it harder for malware to evade detection. The tool addresses the limitations of existing antivirus solutions, which often require extensive permissions and access to personal data. By providing a user-friendly interface and ensuring privacy, this project aims to enhance the overall security of Android devices. Future enhancements include incorporating machine learning models to improve detection accuracy and expanding the tool to other mobile platforms like iOS. This innovative approach offers a reliable and privacy-focused alternative for malware detection on Android devices.
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