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Cluster Computing

, Volume 21, Issue 1, pp 265–275 | Cite as

DroidWard: An Effective Dynamic Analysis Method for Vetting Android Applications

  • Yubin YangEmail author
  • Zongtao Wei
  • Yong Xu
  • Haiwu He
  • Wei Wang
Article

Abstract

As the number of Android malicious applications has explosively increased, effectively vetting Android applications (apps) has become an emerging issue. Traditional static analysis is ineffective for vetting apps whose code have been obfuscated or encrypted. Dynamic analysis is suitable to deal with the obfuscation and encryption of codes. However, existing dynamic analysis methods cannot effectively vet the applications, as a limited number of dynamic features have been explored from apps that have become increasingly sophisticated. In this work, we propose an effective dynamic analysis method called DroidWard in the aim to extract most relevant and effective features to characterize malicious behavior and to improve the detection accuracy of malicious apps. In addition to using the existing 9 features, DroidWard extracts 6 novel types of effective features from apps through dynamic analysis. DroidWard runs apps, extracts features and identifies benign and malicious apps with Support Vector Machine (SVM), Decision Tree (DTree) and Random Forest. 666 Android apps are used in the experiments and the evaluation results show that DroidWard correctly classifies 98.54% of malicious apps with 1.55% of false positives. Compared to existing work, DroidWard improves the TPR with 16.07% and suppresses the FPR with 1.31% with SVM, indicating that it is more effective than existing methods.

Keywords

Android security Malware analysis Malware detection Dynamic analysis 

Notes

Acknowledgements

This work was supported in part by the Scientific Research Foundation through the Returned Overseas Chinese Scholars, Ministry of Education of China, under Grant K14C300020, in part by Shanghai Key Laboratory of Integrated Administration Technologies for Information Security, in part by ZTE Corporation, and in part by the 111 Project under Grant B14005.

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Yubin Yang
    • 1
    Email author
  • Zongtao Wei
    • 2
  • Yong Xu
    • 1
  • Haiwu He
    • 3
  • Wei Wang
    • 2
  1. 1.School of Computer Science & EngineeringSouth China University of TechnologyGuangzhouChina
  2. 2.School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijingChina
  3. 3.Computer Network Information CenterChinese Academy of SciencesBeijingChina

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