Dissecting Android Malware: Characterization and Evolution

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...

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Bibliographic Details
Published in:2012 IEEE Symposium on Security and Privacy pp. 95 - 109
Main Authors: Zhou, Yajin, Jiang, Xuxian
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2012
Subjects:
Android malware
Malware
Mobile communication
Operating systems
Payloads
Security
Smart phones
smartphone security
ISBN:9781467312448, 1467312444
ISSN:1081-6011
Online Access:Get full text
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Summary: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.
ISBN:9781467312448
1467312444
ISSN:1081-6011
DOI:10.1109/SP.2012.16