In recent years, the rapid growth of mobile applications has led to an increase in malicious software targeting mobile devices, aiming to steal users’ information. In this study, we explore and develop machine learning and deep learning models to detect malware on a dataset that we collected and preprocessed from the publicly available dataset of the University of New Brunswick. Our initial models have achieved promising results, laying the foundation for developing an effective malware detection application that helps Android users conveniently prevent malicious software intrusion. In the future, we plan to enhance the dataset quality and improve the performance of our models.
• Malware detection
• Malware images
• Machine Learning
• Mobile application security