Analysis And Detection Of Malware In Android Applications Using Machine Learning

Abstract

The increasing use of Android smartphones and mobile applications has led to a rapid rise in malware attacks targeting user data, privacy, and device security. The project titled “Analysis and Detection of Malware in Android Applications Using Machine Learning” focuses on identifying malicious Android applications through advanced machine learning techniques. The main objective of this project is to develop an intelligent system capable of analyzing application behavior and detecting malware accurately before it affects the user’s device. Traditional antivirus systems mainly rely on signature-based detection methods, which are less effective against newly developed or unknown malware attacks. To overcome this limitation, the proposed system uses machine learning algorithms to analyze permissions, API calls, network behavior, application size, and other important features extracted from Android applications. These features help the system classify applications as benign or malicious. The project involves collecting datasets of Android applications, preprocessing the data, extracting meaningful features, and training machine learning models such as Decision Tree, Random Forest, Support Vector Machine, and Naive Bayes. The trained model can then predict malware threats with improved accuracy and faster detection speed. The system also reduces false positives and improves overall mobile security. This project provides a user-friendly platform where applications can be scanned and analyzed automatically. The system can help users, developers, and organizations protect sensitive information from cyber threats such as spyware, ransomware, trojans, and phishing attacks. Real-time detection and continuous learning capabilities make the proposed solution more efficient than traditional methods.