Detecting Android application malicious behaviors based on the analysis of control flows and data flows-Reference-Cited by-同舟云学术

Detecting Android application malicious behaviors based on the analysis of control flows and data flows

Published:2017-10-13 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 10th International Conference on Security of Information and Networks
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Author:

Zegzhda Peter¹,Zegzhda Dmitry¹,Pavlenko Evgeny¹,Dremov Andrew¹

Affiliation:

1. Peter the Great St. Petersburg Polytechnic University, Russia, St. Petersburg

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3136825.3140583

Reference9 articles.

1. Peter D. Zegzhda Semen S. Kort and Valery G. Chernenko. 2014. To the issue of security analysis of programs in the app stores of mobile devices. Information Security Problems. Computer Systems 1 (Jan. 2014) 52--58. Peter D. Zegzhda Semen S. Kort and Valery G. Chernenko. 2014. To the issue of security analysis of programs in the app stores of mobile devices. Information Security Problems. Computer Systems 1 (Jan. 2014) 52--58.

2. Droid Analytics: A Signature Based Analytic System to Collect, Extract, Analyze and Associate Android Malware

3. Li Lia Tegawende F. Bissyand Mike Papadakisa Siegfried Rasthoferb Alexandre Bartela Damien Octeauc Jacques Kleina and Yves Le Traon. 2016. Static Analysis of Android Apps: A Systematic Literature Review. Technical Report. Interdisciplinary Centre for Security Reliability and Trust (SnT) University of Luxembourg Luxembourg Fraunhofer SIT Darmstadt Germany University of Wisconsin and Pennsylvania State University. Li Lia Tegawende F. Bissyand Mike Papadakisa Siegfried Rasthoferb Alexandre Bartela Damien Octeauc Jacques Kleina and Yves Le Traon. 2016. Static Analysis of Android Apps: A Systematic Literature Review. Technical Report. Interdisciplinary Centre for Security Reliability and Trust (SnT) University of Luxembourg Luxembourg Fraunhofer SIT Darmstadt Germany University of Wisconsin and Pennsylvania State University.

4. Structural detection of android malware using embedded call graphs

5. Semantics-Aware Android Malware Classification Using Weighted Contextual API Dependency Graphs

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