Android Malware Detection Using Machine Learning

Mobile phones play a crucial role in today's society given the prevalence of their use in routine activities, from basic ones like alarm clocks to sensitive ones like banking. These gadgets are among the top targets for hackers due of the sensitive and important information they hold. Android-based phones predominate in the phone market. Because of the widespread distribution of malware, Android's open-source nature has also given rise to a number of security concerns. For detecting Android malware, multiple classification techniques (individual and ensemble) have been used. In this research, we propose an Android malware detection system that classifies Android applications as benign or malicious using five different types of classifiers. The performance of well-known individual classifiers is compared using the suggested method, and experiments are also carried out on two datasets of benign and malicious Android applications.