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Cited by 2,133

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Dissecting Android Malware: Characterization and Evolution

Zhou, Yajin, Jiang, Xuxian·2012·2012 IEEE Symposium on Security and Privacy

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.

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Metadata

Authors
Zhou, Y., & Jiang, X.
Venue
2012 IEEE Symposium on Security and Privacy
Publisher
IEEE
Published
2012-05
Pages
95-109
ISSN
1081-6011 (ISSN-L)
OpenAlex
W2125011234
Cited by
2,133
References
0
FWCI
177.46
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Dissecting Android Malware: Characterization and Evolution. https://app.cochat.ai/papers/10.1109/sp.2012.16
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Zhou, Y., & Jiang, X. (2012). Dissecting Android Malware: Characterization and Evolution. 2012 IEEE Symposium on Security and Privacy. https://doi.org/10.1109/sp.2012.16

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.