P. Renuka, K Rajeswari, U Madhumathi, S Surekha, K Sunitha, K Sreelatha 02 7(1) Nowadays malware has become a more and more concerning matter in the security of information and technology proven by the huge increase in the number of attacks seen over the past few years on all kinds of computers the internet and mobile devices Detection of zeroday malware has become a main motivation for security researchers Since one of the most widely used mobile operating systems is Googles Android attackers have shifted their focus on developing malware that specifically targets Android Many security researchers used multiple Machine Learning algorithms to detect these new Android and other malwares In this paper we propose a new system using machine learning classifiers to detect Android malware following a mechanism to classify each APK application as a malicious or a legitimate application The system employs a feature set of 27 features from a newly released dataset CICMalDroid2020 containing 18998 instances of APKs to achieve the best detection accuracy Our results show that the methodology using Random Forest has achieved the best accuracy of 986 compared to other ML classifiers Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026