Design and Development of Android App Malware Detector API Using Androguard and Catboost

• Published in: International journal for research in applied science and engineering technology 2024-04, Vol.12 (4), p.5121-5128
• Main Authors: K, Vamsee Krishna, P, Sujith Kumar, S, Deepak, C, Gopala Krishnan, S, Rajeswari
• Format: Article
• Language: English
• ISSN: 2321-9653; 2321-9653
• DOI: 10.22214/ijraset.2024.61156

Android has been constant target of attacks through the use of malicious applications (malware) that can harm users, for instance, by leaking sensitive data (e.g., bank account details), blocking access to information and demanding monetary compensation for the ransom, or even leveraging social engineering scams. A large number of malicious applications is distributed in a daily basis through different distribution vectors, such as application markets, including official and third party stores, or untrusted sources like Web repositories. The increasing prevalence of Android malware necessitates advanced detection mechanisms to safeguard users and their devices. The aim of the project is to presents an innovative approach to Android malware detection, integrating machine learning, blockchain technology, and web development tools. The proposed system is to develop a Andriod App Malware Detector API capable of identifying and mitigating malicious applications effectively. The system begins with the collection of diverse datasets comprising both benign and malicious Android applications, ensuring the representation of various threat vectors and scenarios. Androguard, a powerful tool for Android app analysis, the system extracts relevant features such as permissions, API calls, and intents, providing valuable insights into the behaviour and characteristics of each application. The AndroMal Model is designed and trained using CatBoost Algorithm and integrated with a blockchain framework for secure metadata storage. Consensus mechanisms and smart contracts enhance transparency and reliability. Privacy considerations, continuous monitoring, and user feedback mechanisms further fortify the system. The proposed design promises an effective, scalable, and user-centric solution for Android malware detection.