An Efficient Multistage Fusion Approach for Smartphone Security Analysis

Abstract

Android smartphone ecosystem is inundated with innumerable applications mainly developed by third party contenders leading to high vulnerability of these devices. In addition, proliferation of smartphone usage along with their potential applications in diverse field entice malware community to develop new malwares to attack these devices. In order to overcome these issues, an android malware detection framework is proposed wherein an efficient multistage fusion approach is introduced. For this, a robust unified feature vector is created by fusion of transformed feature matrices corresponding to multi-cue using non-linear graph based cross-diffusion. Unified feature is further subjected to multiple classifiers to obtain their classification scores. Classifier scores are further optimally fused employing Dezert-Smarandache Theory (DSmT). Strength of suggested model is assessed both qualitatively and quantitatively by ten-fold cross-validation on the benchmarked datasets. On an average of outcome, we achieved detection accuracy of 98.97% and F-measure of 0.9936.