International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Volume 7 Issue 3
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AndroMal: A Blockchain-Based Machine Learning Framework for Android Malware Detection.

Author(s) Prof. Angelin Rosy M, Ms. Aswini bai S
Country India
Abstract Due to many forms of malware, Android users are vulnerable to attacks that steal their data, block information until a ransom is paid or make it easier for scammers. More criminals are uploading malicious software into official storefronts, third-party markets and web guardians show that stronger malware detection systems are a necessity. In this project, machine learning, blockchain technology and web development tools work together to bring a new technique for detecting Android malware. An Android App Malware Detector API is part of the system, helping to identify and stop hazardous applications. The process begins by assembling datasets with a variety of Android apps, some good and some dangerous, to protect against many kinds of threats. Androguard is a useful tool that helps the system find permissions, API calls and intents for each app, exposing how each application functions. The established AndroMal malware detection model relies on CatBoost to foresee threats and the system stores its metadata through blockchain. The system is made transparent and reliable because of consensus mechanisms and smart contracts. The system we propose is equipped with privacy measures, ongoing monitoring and methods for accepting user input to make it more reliable. With this design, detecting Android malware becomes more efficient, easy for users and flexible, defending against ongoing changes in the mobile world.
Keywords Malware, Detection of malware, Blockchain, Machine learning, Cyber security, Security for Android, Threat intelligence
Field Engineering
Published In Volume 7, Issue 3, May-June 2025
Published On 2025-06-05
DOI https://doi.org/10.36948/ijfmr.2025.v07i03.46450
Short DOI https://doi.org/g9pzsx
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E-ISSN 2582-2160
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