One script to create a permission-based dataset of android applications for your next ML Malware Detection gizmo.

Android Malware Detection is a machine learning-based security tool designed to identify and classify malicious Android applications. The project leverages advanced ML algorithms to analyze Android APK files and detect potential malware threats, helping to protect users from malicious software.

ML algorithms for Android malware detection

Android malware detection from static features with ML classification algorithms- Decision Tree, K-Nearest Neighbours, Linear SVM and Random Forest.

I have worked on an android security project as a consultant for implementing Machine Learning and Deep Learning models for android malware detection The Machine Learning model was developed with Random Forest (RF), Logistic Regression, and Support Vector Machine (SVM) Classifiers The Deep Learning model was developed using Google’s Transformer based Masking model ‘BERT’ and MLP (Multilayer Perceptron) I evaluated the performance difference between ML and DL classifiers in detecting zero-day attacks and compared the results with state-of-the-art methods

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Researched and constructed a machine learning model to detect and distinguish obfuscated or silent malicious behavior on Android devices

Web app built using flask to detect malicious apk files using deep learning model

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Machine learning-based Android malware detector using Natural Language Processing techniques. This project extracts and processes app permissions and API call sequences as features, then applies ML models (e.g., SVM, Random Forest, etc.) for classification. Focuses on explainability, lightweight detection, and robustness across app categories.

An extension of the HinDroid malware detection system, but using metapath2vec to encode apps in the Heterogeneous Information Network. We then hope to make the model resilient to adversarial ML like Android HIV.

Android Malware Detection Using ML

Android malware detection using machine learning and ensemble models

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Project Description:

Generating ML powered malware detection in Android.

ML Project- Android Malware Detection and Classification

Tool for training and deploying ML models for Android malware detection

Hybrid Android malware detection system using static (Androguard) and dynamic (Frida + Genymotion) analysis. Trains multiple ML models (RF, SVM, NN, GNN) to classify APKs with high accuracy, featuring an automated pipeline and a web-based interface.

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ANDROID MALWARE DETECTION USING ML

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Android Malware Detection using ML

Android Malware Detection Using ML

The study evaluates multiple classification algorithms, compares their performance, and analyzes the impact of feature engineering and class imbalance handling on detection accuracy.

Automated Android malware detection system using ensemble machine learning models for high accuracy threat detection.

Analysis and Detection of Malware Android Application Using Machine Learning

ML aplicada a detección de malware para android: regresion logística, perceptrón binario y arbol de decisión binario

*A machine learning-based approach for detecting Android malware using static analysis. This project evaluates Random Forest, SVM, Logistic Regression, KNN, and Naïve Bayes on binary and multi-class classification tasks, achieving up to 96.94% accuracy. Includes trained models, dataset preprocessing scripts, and comprehensive results.*