What question did this study set out to answer?

The central aim is to create an automated system for accurately detecting malicious Android applications without executing them.

April 20, 2026Open Access

Malware Detection Using Machine Learning

Key Points

The central aim is to create an automated system for accurately detecting malicious Android applications without executing them.
Utilized a permission-based dataset representing applications via behavioral features.
Implemented machine learning algorithms including Random Forest, Decision Tree, and Naive Bayes.
Evaluated models using 10-fold cross-validation and standard performance metrics.
Achieved effective malware detection through the application of various machine learning algorithms.
Models demonstrated high accuracy in identifying malicious applications based on permissions.
Evaluation metrics proved the reliability of the detection system.

Abstract

This project focuses on detecting Malicious Android applications using supervised machine learning techniques. A permission based dataset is used where each application is represented by behavioral features such as requested permission. After preprocessing the dataset, machine learning algorithms including Random Forest, Decision tree and Naive Bayes are implemented using the WEKA framework in Java. The models are evaluated using 10-fold cross validation and standard performance metrics. The objective of the project is to develop an automated, accurate and safe malware detection system without executing malicious code.

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Cite This Study

Azhar et al. (Thu,) studied this question.

synapsesocial.com/papers/69e5c3ce03c29399140298be https://doi.org/https://doi.org/10.5281/zenodo.19639504

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