Detection of Android Malware Using Multiple Linear Regression Models-Based Classifiers

computer Engineering

Android is an operating system which presently has over one billion active druggies for all their mobile bias, with a request impact that's impacting an increase in the quantum of information that can be attained from different druggies, data that have motivated the development of malware by cyber culprits. In this study, a frame for Android malware discovery grounded on warrants is presented. This frame uses multiple direct regression styles. operation warrants, which are one of the most critical structure blocks in the security of the Android operating system, are uprooted through static analysis, and security analyzes of operations are carried out with machine literacy ways. Grounded on the multiple direct regression ways, two classifiers are proposed for authorization- grounded Android malware discovery. These classifiers are compared on four different datasets with introductory machine literacy ways similar as support vector machine, k- nearest neighbor, Naive Bayes, and decision trees. In addition, using the bagging system, which is one of the ensemble literacy, different classifiers are created, and the bracket performance is increased. As a result, remarkable performances are attained with bracket algorithms grounded on direct regression models without the need for veritably complex bracket algorithms.

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