Open Access
Issue
MATEC Web Conf.
Volume 255, 2019
Engineering Application of Artificial Intelligence Conference 2018 (EAAIC 2018)
Article Number 05005
Number of page(s) 7
Section Deep Learning and Big Data Analytic
DOI https://doi.org/10.1051/matecconf/201925505005
Published online 16 January 2019
  1. R. Cannings. (2010, 1st September). Exercising Our Remote Application Removal Feature. Available: https://android-developers.googleblog.com/2010/06/exercising-our-remote-application.html [Google Scholar]
  2. H. Lockheimer. (2012, 1st September). Android and Security. Available: http://googlemobile.blogspot.com/2012/02/android-and- security.html, [Google Scholar]
  3. J. Oberheide and C. Miller, “Dissecting the android bouncer,” in SummerCon, New York, USA, 2012. [Google Scholar]
  4. K. D. Harris. (2012, 1st September). Secures Global Agreement to Strengthen Privacy Protections for Users of Mobile Applications. Available: https://oag.ca.gov/news/press-releases/attorney-general-kamala-d-harris-secures-global-agreement-strengthen-privacy [Google Scholar]
  5. L. Xu, Techniques and Tools for Analyzing and Understanding Android Applications: University of California, Davis, 2013. [Google Scholar]
  6. A. Tilton. (2012, 1st September). Vlingo Makes Official Statement: Success at Customers’ Expense. Available: https://www.androidpit.com/vlingo-user-data-leak [Google Scholar]
  7. V. Moonsamy, J. Rong, and S. Liu, “Mining permission patterns for contrasting clean and malicious android applications,” Future Generation Computer Systems, vol. 36, pp. 122–132, 2013. [CrossRef] [Google Scholar]
  8. Google. (2014, 1st April). permission. Available: http://developer.android.com/guide/topics/manifest/permission-element.html [Google Scholar]
  9. A. P. Felt, E. Chin, S. Hanna, D. Song, and D. Wagner, “Android permissions demystified,” in 18th ACM Conference on Computer and Communications Security, Chicago, Illinois, USA, 2011, pp. 627–638. [Google Scholar]
  10. A. P. Felt, M. Finifter, E. Chin, S. Hanna, and D. Wagner, “A survey of mobile malware in the wild,” in the 1st ACM workshop on Security and privacy in smartphones and mobile devices, Chicago, Illinois, USA, 2011, pp. 3–14. [Google Scholar]
  11. Y. Jing, G.-J. Ahn, Z. Zhao, and H. Hu, “Riskmon: Continuous and automated risk assessment of mobile applications,” in Proceedings of the 4th ACM Conference on Data and Application Security and Privacy, 2014, pp. 99–110. [Google Scholar]
  12. M. Grace, Y. Zhou, Q. Zhang, S. Zou, and X. Jiang, “RiskRanker: scalable and accurate zero-day android malware detection,” in 10th International Conference on Mobile Systems, Applications, and Services, Low Wood Bay, Lake District, UK, 2012, pp. 281–294. [Google Scholar]
  13. M. Zheng, M. Sun, and J. Lui, “DroidAnalytics: A Signature Based Analytic System to Collect, Extract, Analyze and Associate Android Malware,” in 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, Melbourne, Australia, 2013, pp. 163–171. [Google Scholar]
  14. R. K. Shahzad and N. Lavesson, “Veto-based malware detection,” in Availability, Reliability and Security (ARES), 2012 Seventh International Conference on, 2012, pp. 47–54. [Google Scholar]
  15. W. Xu, F. Zhang, and S. Zhu, “Permlyzer: Analyzing permission usage in android applications,” in Software Reliability Engineering (ISSRE), 2013 IEEE 24th International Symposium on, 2013, pp. 400–410. [Google Scholar]
  16. D. Barrera, H. G. Kayacik, P. C. V. Oorschot, and A. Somayaji, “A methodology for empirical analysis of permission-based security models and its application to android,” in 17th ACM Conference on Computer and Communications Security, Chicago, Illinois, USA, 2010, pp. 73–84. [Google Scholar]
  17. X. Wei, L. Gomez, I. Neamtiu, and M. Faloutsos, “Permission evolution in the android ecosystem,” in Proceedings of the 28th Annual Computer Security Applications Conference, 2012, pp. 31–40. [Google Scholar]
  18. R. Pandita, X. Xiao, W. Yang, W. Enck, and T. Xie, “WHYPER: towards automating risk assessment of mobile applications,” in 22nd USENIX Security Symposium, Washington, D.C, USA, 2013, pp. 527–542. [Google Scholar]
  19. J. Zhu, Z. Guan, Y. Yang, L. Yu, H. Sun, and Z. Chen, “Permission-based abnormal application detection for android,” in International Conference on Information and Communications Security, 2012, pp. 228–239. [Google Scholar]
  20. Y. Z. X. Jiang and Z. Xuxian, “Detecting passive content leaks and pollution in android applications,” in Proceedings of the 20th Network and Distributed System Security Symposium (NDSS), 2013. [Google Scholar]
  21. S. Liang, M. Might, and D. Van Horn, “Anadroid: Malware analysis of android with user-supplied predicates,” arXiv preprint arXiv:1311.4198, 2013. [Google Scholar]
  22. D. Sbîrlea, M. G. Burke, S. Guarnieri, M. Pistoia, and V. Sarkar, “Automatic detection of inter-application permission leaks in Android applications,” IBM Journal of Research and Development, vol. 57, pp. 10: 1–10: 12, 2013. [Google Scholar]
  23. W. Dong-Jie, M. Ching-Hao, W. Te-En, L. Hahn-Ming, and W. Kuo-Ping, “DroidMat: Android Malware Detection through Manifest and API Calls Tracing,” in Seventh Asia Joint Conference on Information Security (Asia JCIS), Tokyo, Japan, 2012, pp. 62–69. [Google Scholar]
  24. D. Arp, M. Spreitzenbarth, M. Hubner, H. Gascon, and K. Rieck, “DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket,” in 2014 Network and Distributed System Security (NDSS) Symposium, San Diego, USA, 2014, pp. 1–15. [Google Scholar]
  25. Y. Aafer, W. Du, and H. Yin, “DroidAPIMiner: Mining API-Level Features for Robust Malware Detection in Android,” in 9th International Conference on Security and Privacy in Communication Networks, Sydney, Australia, 2013, pp. 86–103. [Google Scholar]
  26. Lookout. (2010, 21st April). Security Alert: Geinimi, Sophisticated New Android Trojan Found in Wild. Available: https://blog.lookout.com/blog/2010/12/29/geinimi_trojan/ [Google Scholar]
  27. S. K. Dash, G. Suarez-Tangil, S. Khan, K. Tam, M. Ahmadi, J. Kinder, et al., “DroidScribe: Classifying Android Malware Based on Runtime Behavior,” in Mobile Security Technologies (MoST 2016), San Jose, USA, 2016, pp. 1–12. [Google Scholar]
  28. M. Varsha, P. Vinod, and K. Dhanya, “Identification of malicious android app using manifest and opcode features,” Journal of Computer Virology and Hacking Techniques, pp. 1–14, 2016. [Google Scholar]
  29. K. Allix, T. F. Bissyand, J. Klein, and Y. L. Traon, “AndroZoo: collecting millions of Android apps for the research community,” in 13th International Conference on Mining Software Repositories, Austin, USA, 2016, pp. 468–471. [Google Scholar]
  30. A. Mikami, “Long Short-Term Memory Recurrent Neural Network Architectures for Generating Music and Japanese Lyrics,” Ph.D., Computer Science Department, Boston College, 2016. [Google Scholar]
  31. H.-J. Zhu, T.-H. Jiang, B. Ma, Z.-H. You, W.-L. Shi, and L. Cheng, “HEMD: a highly efficient random forest-based malware detection framework for Android,” Neural Computing and Applications, pp. 1–9, 2017. [Google Scholar]
  32. C. Bae and S. Shin, “A collaborative approach on host and network level android malware detection,” Security and Communication Networks, vol. 9, pp. 5639–5650, 2016. [CrossRef] [Google Scholar]
  33. K. Sokolova, C. Perez, and M. Lemercier, “Android application classification and anomaly detection with graph-based permission patterns,” Decision Support Systems, vol. 93, pp. 62–76, 2017. [CrossRef] [Google Scholar]
  34. B. Sanz, I. Santos, X. Ugarte-Pedrero, C. Laorden, Nieves, and P. G. Bringas, “Instance-based Anomaly Method for Android Malware Detection,” in 10th International Conference on Security and Cryptography, Reykjavík, Iceland, 2013, pp. 387–394. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.