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research-article

Two Phase Static Analysis Technique for Android Malware Detection

Authors:

Priyadarshani M. Kate,

Sunita V. DhavaleAuthors Info & Claims

WCI '15: Proceedings of the Third International Symposium on Women in Computing and Informatics

Pages 650 - 655

https://doi.org/10.1145/2791405.2791558

Published: 10 August 2015 Publication History

Abstract

The growing popularity of Android based smart phones has greatly fuelled the spread of android malware. Further, these malwares are evolving rapidly to escape from traditional signature-based detection methods. Hence, there is a serious need to develop effective android malware detection techniques. In this paper, we propose two phase static android malware analysis scheme using bloom filters. The Phase I involves two different bloom filters that classify a given sample into malware or benign class based on permission feature set only. The evaded malicious samples from Phase I are further analyzed by Phase II consisting Naïve Bayes Classifier using permission and code based mixed feature set. Inclusion of Phase I classification makes the technique computationally less intensive; while addition of the Phase II classification improves the overall accuracy of the proposed model. Experimental results indicate both detection accuracy and computational efficiency of the proposed technique.

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Digital Library

Cited By

Sharma TRattan D(2021)Malicious application detection in android — A systematic literature reviewComputer Science Review10.1016/j.cosrev.2021.10037340(100373)Online publication date: May-2021
https://doi.org/10.1016/j.cosrev.2021.100373
Falana OSodiya AOnashoga SOyewole A(2021)PEDAM: Priority Execution Based Approach for Detecting Android MalwareInternational Conference on Emerging Applications and Technologies for Industry 4.0 (EATI’2020)10.1007/978-3-030-80216-5_12(152-165)Online publication date: 15-Jul-2021
https://doi.org/10.1007/978-3-030-80216-5_12
Zhang JZhuang XChen Y(2019)Android Malware Detection Combined with Static and Dynamic AnalysisProceedings of the 2019 9th International Conference on Communication and Network Security10.1145/3371676.3371685(6-10)Online publication date: 15-Nov-2019
https://dl.acm.org/doi/10.1145/3371676.3371685
Show More Cited By

Index Terms

Two Phase Static Analysis Technique for Android Malware Detection
1. Security and privacy
  1. Systems security
    1. Operating systems security

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Published In

cover image ACM Other conferences

WCI '15: Proceedings of the Third International Symposium on Women in Computing and Informatics

August 2015

763 pages

ISBN:9781450333610

DOI:10.1145/2791405

Editor:
Indu Nair
SCMS, Kochi, India
,
General Chairs:
Sushmita Mitra
Indian Statistical Institute, Kolkata, India
,
Ljiljana Trajković
Simon Fraser University, Canada
,
Program Chairs:
Punam Bedi
University of Delhi, India
,
Suzanne McIntosh
New York University and Cloudera Inc., USA
,
M. S. Rajasree
IIITM-K, Trivandrum, India

Copyright © 2015 ACM.

© 2015 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 August 2015

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WCI '15

WCI '15: Third International Symposium on Women in Computing and Informatics

August 10 - 13, 2015

Kochi, India

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WCI '15 Paper Acceptance Rate 98 of 452 submissions, 22%;

Overall Acceptance Rate 98 of 452 submissions, 22%

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Citations

Cited By

Sharma TRattan D(2021)Malicious application detection in android — A systematic literature reviewComputer Science Review10.1016/j.cosrev.2021.10037340(100373)Online publication date: May-2021
https://doi.org/10.1016/j.cosrev.2021.100373
Falana OSodiya AOnashoga SOyewole A(2021)PEDAM: Priority Execution Based Approach for Detecting Android MalwareInternational Conference on Emerging Applications and Technologies for Industry 4.0 (EATI’2020)10.1007/978-3-030-80216-5_12(152-165)Online publication date: 15-Jul-2021
https://doi.org/10.1007/978-3-030-80216-5_12
Zhang JZhuang XChen Y(2019)Android Malware Detection Combined with Static and Dynamic AnalysisProceedings of the 2019 9th International Conference on Communication and Network Security10.1145/3371676.3371685(6-10)Online publication date: 15-Nov-2019
https://dl.acm.org/doi/10.1145/3371676.3371685
Wang WZhao MGao ZXu GXian HLi YZhang X(2019)Constructing Features for Detecting Android Malicious Applications: Issues, Taxonomy and DirectionsIEEE Access10.1109/ACCESS.2019.29181397(67602-67631)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2918139
Kim DMirsky DMajlesi-Kupaei ABarua R(2018)A Hybrid Static Tool to Increase the Usability and Scalability of Dynamic Detection of Malware2018 13th International Conference on Malicious and Unwanted Software (MALWARE)10.1109/MALWARE.2018.8659373(115-123)Online publication date: Oct-2018
https://doi.org/10.1109/MALWARE.2018.8659373
Al Shamsi FWoon WAung Z(2018)Discovering Similarities in Malware Behaviors by Clustering of API Call SequencesNeural Information Processing10.1007/978-3-030-04212-7_11(122-133)Online publication date: 17-Nov-2018
https://doi.org/10.1007/978-3-030-04212-7_11
Zhang SXiao X(2017)CSCdroid: Accurately Detect Android Malware via Contribution-Level-Based System Call Categorization2017 IEEE Trustcom/BigDataSE/ICESS10.1109/Trustcom/BigDataSE/ICESS.2017.237(193-200)Online publication date: Aug-2017
https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.237
Sadeghi ABagheri HGarcia JMalek S(2017)A Taxonomy and Qualitative Comparison of Program Analysis Techniques for Security Assessment of Android SoftwareIEEE Transactions on Software Engineering10.1109/TSE.2016.261530743:6(492-530)Online publication date: 1-Jun-2017
https://doi.org/10.1109/TSE.2016.2615307
Du YWang JLi Q(2017)An Android Malware Detection Approach Using Community Structures of Weighted Function Call GraphsIEEE Access10.1109/ACCESS.2017.27201605(17478-17486)Online publication date: 2017
https://doi.org/10.1109/ACCESS.2017.2720160
Kalutarage HNguyen HShaikh S(2017)Towards a threat assessment framework for apps collusionTelecommunications Systems10.1007/s11235-017-0296-166:3(417-430)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1007/s11235-017-0296-1
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