A New Mobile Malware Classification for Audio Exploitation

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    Rapid growth and usage of Android smartphones worldwide have attracted many attackers to exploit them. Currently, the attackers used mobile malware to attack victims’ smartphones to steal confidential information such as username and password. The attacks are also motivated based on profit and money. The attacks come in different ways, such as via audio, image, GPS location, SMS and call logs in the smartphones. Hence, this paper presents a new mobile malware classification for audio exploitation. This classification is beneficial as an input or database to detect the mobile malware attacks. System calls and permissions for audio exploitation have been extracted by using static and dynamic analyses using open source tools and freeware in a controlled lab environment. The testing was conducted by using Drebin dataset as the training dataset and 500 anonymous apps from Google Play store as the testing dataset. The experiment results showed that 2% suspicious malicious apps matched with the proposed classification. The finding of this paper can be used as guidance and reference for other researchers with the same interest.



  • Keywords

    Audio Exploitation; Android Smartphone; Malicious Apps; Mobile Malware.

  • References

      [1] Soriano, A.,” A. Software, Avast Blog mobile malware”, (2016), https://blog.avast.com/topic/mobile-malware.

      [2] Lemos, R, “New malware threats emerge on mobile platforms”, (2016), http://www.eweek.com/security/new-malware-threats-emerge-on-mobile-platforms-studies-find.

      [3] Schlegel, R. Zhang, K. & Zhou, X. “Soundcomber: A stealthy and context-aware sound Trojan for smartphones”, Proceedings of the 18th Annual Network and Distributed System Security Symposium, (2011), pp. 17–33.

      [4] Alcatel-Lucent, “Mobile malware: A network view”, (2015), https://www.blackhat.com/docs/ldn-15/materials/london-15-McNamee-Mobile-Malware-A-Network-View-wp.pdf.

      [5] Gartner, “Gartner says worldwide sales of smartphones grew 9 percent in first quarter of 2017”, (2017), http://www.gartner.com/newsroom/id/3725117.

      [6] Junaid, M. Donggang, L. & David, K. (2016), Dexteroid: Detecting malicious behaviors in android apps using reverse-engineered life cycle models. Computers and Security, 59, 92–117.

      [7] Ping, W. & Wang, Y.-S. (2015), Malware behavioural detection and vaccine development by using a support vector model classifier. Journal of Computer and System Sciences, 81(6), 1012–1026.

      [8] Lindorfer, M., Neugschwandtner, M., Weichselbaum, L. Fra-tantonio, Y., van der Veen, V. & Platzer, C. (2014), ANDRUBIS -- 1,000,000 apps later: A view on current android malware behaviors. Proceedings of the 3rd International Workshop on Building Analysis Datasets and Gathering Experience Returns for Security, pp. 3–17.

      [9] Feizollah, A., Anuar, N. B., Salleh, R. & Abdul Wahab, A. W. (2015), A review on feature selection in mobile malware detection. Digital Investigation, 13, 22–37.

      [10] Hashim, H. A.-B., Saudi, M. M., & Basir, N. “A systematic review analysis of root exploitation for mobile botnet detection”, Proceedings of the 1st International Conference on Communication and Computer Engineering, (2015), pp. 925-938.

      [11] Bhatt, M. S., Patel, H. & Kariya, S. (2015), A survey permission based mobile malware detection. International Journal of Computer Technology and Applications, 6(5), 852–856.

      [12] Karim, A., Salleh, R. & Khan, M. K. (2007), SMARTbot: A behavioral analysis framework augmented with machine learning to identify mobile botnet applications. PLoS One, 11(3), p. e0150077.

      [13] Wu, S., Wang, P. Li, X. & Zhang, Y. (2016), Effective detection of android malware based on the usage of data flow APIs and machine learning. Information and Software Technologies, 75, 17–256.

      [14] Saudi, M. M. & Husainiamer, M. A. (2017), Mobile malware classification via system calls and permission for GPS exploitation. International Journal of Advanced Computer Science and Applications, 8(6), 277-283.




Article ID: 21372
DOI: 10.14419/ijet.v7i4.15.21372

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.