Efficiency of Malware Detection in Android System: A Survey
Maria Ayoub Omer, Subhi R. M. Zeebaree, Mohammed A. M. Sadeeq, Baraa Wasfi Salim, Sanaa Mohsin, Zryan Najat Rashid, Lailan M. Haji
Abstract
Smart phones are becoming essential in our lives, and Android is one of the most popular operating systems. Android OS is wide-ranging in the mobile industry today because of its open-source architecture. It is a wide variety of applications and basic features. App users tend to trust Android OS to secure data, but it has been shown that Android is more vulnerable and unstable. Identification of Android OS malware has become an emerging research subject of concern. This paper aims to analyze the various characteristics involved in malware detection. It also addresses malware detection methods. The current detection mechanism utilizes algorithms such as Bayesian algorithm, Ada grad algorithm, Naïve Bayes algorithm, Hybrid algorithm, and other algorithms for machine learning to train the sets and find the malware.