Behavior-Based Malware Detection System Approach For Mobile Security Using Machine Learning

Abstract

In today's world, mobile security is critical not only for our society but also for each individual. Today, everyone wants their own mobile device, which has resulted in a growth in the number of Android users around the world. Each device with internet access interacts with a variety of applications, resulting in a large number of malware infections or dangers in a mobile home. Our strategy moving forward will be to keep everyone's mobile device secure. So, using machine learning, we've created a model for a behavior-based anomaly detection system from an Android mobile device. We used three machine algorithms in this system to detect malware vulnerabilities based on the behaviour of mobile applications. To determine the accuracy of mobile application behaviour in this system, we employed KNN, Naive Bayes, and a decision tree method. As a result, this technique can be utilised to keep a person's Android mobile secure.