Malware Detection in Android files based on Multiple levels of Learning and Diverse Data Sources

Abstract

Smart mobile device usage has expanded at a very high rate all over the world. Mobile devices have experienced a rapid shift from pure telecommunication devices to small ubiquitous computing platforms. They run sophisticated operating systems that need to confront the same risks as desktop computers, with Android as the most targeted platform for malware. The processing power is one of the factors that differentiate PC's and mobile phones. Mobile phones are more compact and therefore limited in memory and depend on a limited battery power for their energy needs. Hence developing apps to run on these devices should take into consideration the above mentioned factors. To improve the speed of detection, a multilevel detection mechanism using diverse data sources is designed for detecting malware balancing between the accuracy of detection and usage of less compute intensive computations. In this work we have analyzed android based malware for analysis and a multilevel detection mechanism is designed using diverse data sources. We have evaluated our work on a collection of Android based malware comprising of different malware families and our results show that the proposed method is faster with good performance