AI-Powered Android Malware Detection using Machine Learning | Python IEEE Project 2025
AI-Powered Android Malware Detection using Machine Learning | Python Final Year IEEE Project 2025.
🛒Buy Link:
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To buy this Python project Source Code in ONLINE, Contact:
🔗Email: jpinfotechprojects@gmail.com,
🌐Website: https://www.jpinfotech.org
📌Our Proposed Project Title: AI-Powered Android Malware Detection using Machine Learning.
💡Implementation: Python.
🔬Algorithm / Model Used: Logistic Regression, ExtraTree Classifier, Random Forest Classifier, Stacking Classifier
🌐Web Framework: Flask.
🖥️Frontend: HTML, CSS, JavaScript.
💰Cost (In Indian Rupees): Rs.5000/
📘Project Abstract:
👉The rapid growth of Android applications has significantly increased the risk of malware attacks, leading to data theft, unauthorized access, privacy breaches, and system-level exploitation.
👉To address this challenge, this project proposes an AI-Powered Android Malware Detection System that integrates machine learning techniques for effective identification of malicious applications.
👉 The system is developed using Python for backend processing, Flask as the web framework, and HTML, CSS, and JavaScript for the frontend interface, providing an interactive and user-friendly web-based platform.
🚀IEEE Base Paper Title:
Enhancing the Sustainability of Machine Learning-Based Malware Detection Techniques for Android Applications.
📍REFERENCE:
SEYEON PARK, HOJUN LEE, DAEUN KIM1, HYEUN JUN MOON, SEONG-JE CHO, YOUNGSUP HWANG, HYOIL HAN, AND KYOUNGWON SUH, “Enhancing the Sustainability of Machine Learning-Based Malware Detection Techniques for Android Applications”, IEEE Access, Volume: 13, 2025.
🕑 Timeline:
00:00 - Intro.
00:00 - IEEE Base Paper Concept.
00:00 - Our Proposed Project Abstract.
00:00 - Dataset Details.
00:00 - Existing System.
00:00 - Proposed System.
00:00 - System Architecture.
00:00 - System Requirements.
00:00 - Project Execution.
00:00 - Frequently Asked Questions (FAQ's)
🏷️tags:
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