COMPARATIVE STUDY FOR ANDRIOD MOBILE STATIC ANALYSIS ALGORITHMS

Abstract

Recently, there has been a rapid increase in the use of smartphones, several of which are connected to the internet. Because of the data movement, malware attacks have enormously increased. Malware causes unexpected behavior in smartphones such as strange charges on your phone bill, invasive adverts, contacts receiving strange messages, poor performance, appearance of new applications, abnormal data consumption and noticeable reduction in battery life. Nonetheless, smartphone users remain unprotected from malware attacks. Thus, mobile antivirus applications have been developed to overcome this issue. Since android has established itself as the industry's dominant operating system for smartphones, many antivirus applications are available in the android play store. This paper presents a comparative study of android mobile static analysis. Static analysis is used to classify malware android Apps through meta data file of APK. Furthermore, we used TF-IDF feature extractor and investigate algorithms for static analysis, such as decision tree, naïve bayes, random Forest, K-nearest neighbor, XGB, MLP, support vector machine, logistic regression, adaboost,,lasso regression, ride regression, ANN and extra trees. We use two datasets small and large “Drebin”. The results of small dataset show that Multi-layer perceptron (MLP) gives the best overall accuracy 98.84% but it takes the biggest execution time around 33.4 seconds and The results of large dataset show that Extra trees gives the best overall accuracy 99.48%.