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· 2012

Dissecting Android Malware: Characterization and Evolution

Yajin Zhou, Xuxian Jiang

Short summary

Researchers characterized over 1,200 Android malware samples collected between August 2010 and October 2011, revealing rapid evolution that circumvents current antivirus software.

AI-generated from the title and abstract; the full text is not read.

Key points

  • Collected and analyzed over 1,200 Android malware samples from August 2010 to October 2011.
  • Characterized malware by installation methods, activation mechanisms, and malicious payloads.
  • Observed rapid evolution of malware families to evade detection.
  • Current antivirus software detection rates for the dataset range from 20.2% to 79.6%.

AI-generated from the title and abstract; the full text is not read.

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.

The authors' abstract, as published at the source., 2012 · DOI ↗

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Field: Signal Processing

Signal ProcessingComputer Science