A framework for automated extraction of static and dynamic features from Android applications

Android Mobile Device Hardening

Papers, code and datasets about deep learning for Android malware defenses and malware detection

Android Malware Samples

Drebin - NDSS 2014 Re-implementation

A list of awesome malware detection tools

extract info from apk files

Android Malware Detection Using Machine Learning Project with Source Code and Documents Plus Video Explanation

Android Malware Detection Using Machine Learning Classifiers ( Using Permissions requested by Apps)

Android Malware Detection using Deep Learning

Android Malware Detection with Graph Convolutional Networks using Function Call Graph and its Derivatives.

Android malware detection using static and dynamic analysis

⚙️ An efficient tool to do in-depth comparison of two android apps.

A Federated Learning based Android Malware Classification System

CNN based android malware detection system

Structural Attack against Graph-based Android Malware Detection System

Detection of Android Malware based on system calls using Support Vector Machines.

Cybersecurity Data Mining Competition 2016

Android malware classification using both .java files and .so files

Android Malware Detection Website

Cybersecurity Data Mining Competition 2017

Phenax is an open source framework to test Android applications whether they are malicious or not. Using a tool called GroddDroid and machine learning algorithms this framework repeatedly runs a number of goodware and malware applications forcing a different execution path in each application in each run.

Storehouse of scripts/code snippets corresponding to the current RnD project.

Android Malware Detection Web Application

Implemented a novel Android malware detection software using natural language processing and deep learning to extract features from the static analysis reports of the applications.

Deep Learning Research

King's College London final dissertation

MAL2 Android-Malware Detection training machine learning detection models and providing API for submitting APK files and getting them analysed

The general goal of this project is to build a web application based on a machine-learning algorithm that can detect fraudulent apps from the Google Play Store or other store using network features values. Demo video: https://www.loom.com/share/29b3ba0b6e644e7d8616c16f88546388

Awesome Dynamic Android Malware Detection Scientific Research