Antimalwareapp

Anti-malware for Android using machine learning

Cybersecurity & Ethical HackingJavaGPL-3.0

Abstract

Antimalwareapp is an open-source Cybersecurity & Ethical Hacking project. Anti-malware for Android using machine learning. LibreAV is an attempt to detect malwares on android devices by utilizing machine learning approach. It is built using Java, Machine Learning, Android. Key capabilities include: Real time scanning; On device inference; Lightweight. The complete source code is publicly available on GitHub under the GNU General Public License v3.0, making it a useful reference for students building a Cybersecurity & Ethical Hacking mini project or final-year project.

1. Introduction

LibreAV is an attempt to detect malwares on android devices by utilizing machine learning approach.

A free and open source anti-malware for android using machine learning.

2. Objective

Anti-malware for Android using machine learning

This project demonstrates how Java, Machine Learning, Android can be applied to a real-world Cybersecurity & Ethical Hacking problem.

3. Key Features / Modules

  • Real time scanning
  • On device inference
  • Lightweight
  • 100% free and no ads

4. Technology Stack

JavaMachine LearningAndroid

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • JDK 11 or later
  • Maven / Gradle
  • IntelliJ IDEA, Eclipse or Android Studio
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/projectmatris/antimalwareapp.git
cd antimalwareapp

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

  • Add logging and alert notifications (email / Telegram)
  • Write a threat model document for the tool
  • Package it with Docker for safe lab testing

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. Which threat or attack does this project defend against?
  2. What detection or protection technique is used and what are its limits?
  3. How are false positives and false negatives handled?
  4. Which cryptographic algorithms or security standards are involved?
  5. What legal and ethical rules apply when testing a tool like this?

9. Source Code & License

This project is developed by projectmatris and published on GitHub under the GNU General Public License v3.0. Please follow the license terms and credit the original author when you use or modify this code.

View Source on GitHubDownload ZIP

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