An Improved Ensemble Based Machine Learning Technique for Efficient Malware Classification

Abstract

Android smartphones have become an emerging technology due to widespread adoption. The widely used Android devices allow installation of apps and grant privileges to access confidential information from the phone which resulted in being targeted by malware developers. The dramatic rise in the number of attacks, develop an interest to make a robust system that automatically identifies the presence of malicious behavior in Android applications. The previous malware detection studies comprised of static and dynamic analysis techniques, extreme learning machine and virtual machine introspection that have few shortcomings in detection of data outflow such as high computational and performance cost, low accuracy, high false positive rates, etc. The proposed approach overcomes the problems of static and dynamic techniques in malware detection. The novel classification approach senses all kinds of source-code and application behaviors. The proposed technique scans the keywords of manifest.xml files for malicious items. By the enhancement of manifest.xml feature the proposed technique can reduce apps scan time as compared to previous proposed malware detection frameworks. This technique also improves the security of Android users.