B. PAVAN KUMAR,POTTABATHULA KEERTHY,TEEGALA TRISHANK,ONTEDDU SANTOSH,VALIMINETI KRISHNA CHAITHANYA 03 03 The rapid growth of smartphone usage driven by affordability and the digitalization of services has introduced significant security challenges Among these malware threats have become a major concern particularly on the Android platform where the proliferation of malicious and fraudulent applications has increased substantially As Android devices continue to gain popularity malware developers consistently create new threats compromising system integrity and user privacy This study aims to apply Machine Learning ML techniques for effective Android malware detection A comprehensive detection framework is proposed incorporating six ML models for classifying different types of malware including Decision Trees Support Vector Machines SVM Naive Bayes Random Forests KNearest Neighbors KNN and Ensemble Methods such as the Extra Trees Classifier The framework is evaluated using the CICMalAnal2017 dataset which includes diverse malware categories such as adware ransomware and scareware To enhance model performance multiple feature selection techniques are employed including Feature Correlation Random Forest Importance ChiSquare Test and Information Gain These methods help identify the most relevant features for accurate classification The performance of various ML algorithms is analyzed and compared to determine the most effective approach for malware detection Furthermore the study highlights the importance of using MLbased techniques to detect vulnerabilities at the source code level as implementing security measures after application deployment can be more challenging Overall this research contributes to a deeper understanding of Android malware detection and provides insights into potential future directions for improving mobile security systems Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026