Dissecting Android Malware: Characterization and Evolution

TLDR

Smartphone proliferation has accelerated the spread of mobile malware, especially on Android, yet defenders lack sufficient understanding and timely sample access. This study aims to systematically characterize Android malware to inform the development of more effective protection strategies. Over a year, the authors collected more than 1,200 samples spanning most Android families from 2010 to 2011 and analyzed their installation methods, activation mechanisms, and payloads. The analysis shows rapid evolution to evade detection, with only 79.6 % of samples detected by the best of four security tools and 20.2 % by the worst, underscoring the need for next‑generation anti‑mobile‑malware solutions.

Abstract

The popularity and adoption of smart phones has greatly stimulated the spread of mobile malware, especially on the popular platforms such as Android. In light of their rapid growth, there is a pressing need to develop effective solutions. However, our defense capability is largely constrained by the limited understanding of these emerging mobile malware and the lack of timely access to related samples. In this paper, we focus on the Android platform and aim to systematize or characterize existing Android malware. Particularly, with more than one year effort, we have managed to collect more than 1,200 malware samples that cover the majority of existing Android malware families, ranging from their debut in August 2010 to recent ones in October 2011. In addition, we systematically characterize them from various aspects, including their installation methods, activation mechanisms as well as the nature of carried malicious payloads. The characterization and a subsequent evolution-based study of representative families reveal that they are evolving rapidly to circumvent the detection from existing mobile anti-virus software. Based on the evaluation with four representative mobile security software, our experiments show that the best case detects 79.6% of them while the worst case detects only 20.2% in our dataset. These results clearly call for the need to better develop next-generation anti-mobile-malware solutions.