Abstract
The number of security incidents faced by Android users is growing, along with a surge in malware targeting Android terminals. Such malware arrives at the Android terminals in the form of Android Packages (APKs). Various techniques for protecting Android users from such malware have been reported, but most of them have focused on the APK files themselves. Unlike these approaches, we use Web information obtained from online APK markets to improve the accuracy of malware detection. In this paper, we propose category/cluster-based APK analysis schemes that quantify the risk of an APK. The category-based scheme uses category information available on the Web, whereas the cluster-based method uses APK descriptions to generate clusters of APK files. In this paper, the performance of the proposed schemes is verified by comparing their area under the curve values with that of a conventional scheme; moreover, the usability of Web information for the purpose of better quantifying the risks of APK files is confirmed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
We believe that better quantification results are achieved if we consider the difference between the requested permissions and those actually used, i.e., permission gaps [1], because this removes noises added to the characteristics of the APK files. Nevertheless, this is beyond the scope of this paper.
- 4.
- 5.
We measure the AUC values by setting I(np) to 1.0 and increasing the value of I(dp) from 1.0 to 3.0Â in increments of 0.1, and then choose the value that provides the highest AUC as the optimal value. Note that dangerous permissions are certainly more harmful than normal permissions.
- 6.
The evaluation following this procedure should be iterated to gain the average values of the studied values. Moreover, cross validation should be applied to the learning process. Our future work will cope with this.
- 7.
The values for I and \(I_c\) were determined empirically, as with Sect. 2.3.
References
Bartel, A., Klein, J., Le Traon, Y., Monperrus, M.: Automatically securing permission-based software by reducing the attack surface: an application to android. In: Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering (2012)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Brown, C.D., Davis, H.T.: Receiver operating characteristics curves and related decision measures: a tutorial. Chemometr. Intell. Lab. Syst. 80(1), 24–38 (2006)
Gorla, A., Tavecchia, I., Gross, F., Zeller, A.: Checking app behavior against app descriptions. In: Proceedings of the 36th International Conference on Software Engineering (2014)
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. Statistics, vol. 1, pp. 281–297 (1967)
Sarma, B.P., Li, N., Gates, C., Potharaju, R., Nita-Rotaru, C., Molloy, I.: Android permissions: a perspective combining risks and benefits. In: Proceedings of the 17th ACM Symposium on Access Control Models and Technologies (2012)
Takahashi, T., Nakao, K., Kanaoka, A.: Data model for android package information and its application to risk analysis system. In: Proceedings of the First ACM Workshop on Information Sharing and Collaborative Security (2014)
Wang, Y., Zheng, J., Sun, C., Mukkamala, S.: Quantitative security risk assessment of android permissions and applications. In: Wang, L., Shafiq, B. (eds.) DBSec 2013. LNCS, vol. 7964, pp. 226–241. Springer, Heidelberg (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Takahashi, T., Ban, T., Mimura, T., Nakao, K. (2015). Fine-Grained Risk Level Quantication Schemes Based on APK Metadata. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9491. Springer, Cham. https://doi.org/10.1007/978-3-319-26555-1_75
Download citation
DOI: https://doi.org/10.1007/978-3-319-26555-1_75
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26554-4
Online ISBN: 978-3-319-26555-1
eBook Packages: Computer ScienceComputer Science (R0)