A Novel Traffic Based Framework for Smartphone Security Analysis

Abstract

Android Operating system (OS) has grown into the most predominant smartphone platform due to its flexibility and open source characteristics. Because of its openness, it has become prone to numerous attackers and malware designers who are constantly trying to elicit confidential information by articulating a plethora of attacks through these designed malwares. Detection of these malwares to protect the smartphone is the core function of the smartphone security analysis. This paper proposes a novel traffic-based framework that exploits the network traffic features to detect these malwares. Here, a unified feature (UF) is created by graph-based cross-diffusion of generated order and sparse matrices corresponding to the network traffic features. Generated unified feature is then given to three classifiers to get corresponding classifier scores. The robustness of the suggested framework when evaluated on the standard datasets outperforms contemporary techniques to achieve an average accuracy of 98.74 per cent.