Visible to the public Dissecting Android Malware: Characterization and Evolution

TitleDissecting Android Malware: Characterization and Evolution
Publication TypeConference Paper
Year of Publication2012
AuthorsYajin Zhou, Xuxian Jiang
Conference NameSecurity and Privacy (SP), 2012 IEEE Symposium on
Date PublishedMay
Keywordsactivation mechanisms, Android malware, Android malware family, Android platform, Androids, carried malicious payloads, computer viruses, defense capability, evolution-based study, Humanoid robots, installation methods, Malware, mobile antivirus software, Mobile communication, mobile computing, mobile malware, next-generation antimobile-malware solutions, operating systems (computers), Payloads, representative family, representative mobile security software, smart phones, smartphone security

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.

Citation Key6234407