Abstract
Android, being the most popular operating system for the mobile devices, has attracted a plethora of malware that are being distributed through various applications (apps). The malware apps cause serious security and privacy concerns, such as accessing/leaking sensitive information, sending messages to the paid numbers, etc. Like traditional analysis and detection approaches for desktop malware applications, there have been many proposals to apply machine learning techniques to detect malicious apps. However unlike classical desktop applications, Android apps available on the “Google Play” [1] have a feature in “category” of app. In this initial work, we propose and investigate the possibility of improving the efficiency of machine learning approach for android apps by exploiting the category information. Experiment results performed over a large dataset, are encouraging which shows the effectiveness of our simple yet productive approach.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Felt, A.P., Chin, E., Hanna, S., Song, D., Wagner, D.: Android permissions demystified. In: ACM Conference on Computer and Communications Security (2011)
Zhou, Y., Jiang, X.: Dissecting android malware: Characterization and evolution. In: Security and Privacy (2012)
Aafer, Y., Du, W., Yin, H.: DroidAPIMiner: Mining API-level features for robust malware detection in android. In: Zia, T., Zomaya, A., Varadharajan, V., Mao, M. (eds.) SecureComm 2013. LNICST, vol. 127, pp. 86–103. Springer, Heidelberg (2013)
Chin, E., Felt, A.P., Greenwood, K., Wagner, D.: Analyzing inter-application communication in android. In: International Conference on Mobile Systems, Applications, and Services (2011)
Wei, X., Gomez, L., Neamtiu, I., Faloutsos, M.: Profiledroid: Multi-layer profiling of android applications. In: International Conference on Mobile Computing and Networking (2012)
Grace, M., Zhou, Y., Zhang, Q., Zou, S., Jiang, X.: Riskranker: scalable and accurate zero-day android malware detection. In: International Conference on Mobile Systems, Applications, and Services (2012)
Fuchs, A.P., Chaudhuri, A., Foster, J.S.: Scandroid: Automated security certification of android applications. Manuscript, Univ. of Maryland (2009), http://www.cs.umd.edu/~avik/projects/scandroidascaa
Barrera, D., Kayacik, H.G., van Oorschot, P.C., Somayaji, A.: A methodology for empirical analysis of permission-based security models and its application to android. In: ACM Conference on Computer and Communications Security (2010)
Di Cerbo, F., Girardello, A., Michahelles, F., Voronkova, S.: Detection of malicious applications on android OS. In: Sako, H., Franke, K.Y., Saitoh, S. (eds.) IWCF 2010. LNCS, vol. 6540, pp. 138–149. Springer, Heidelberg (2011)
Zhou, Y., Zhang, X., Jiang, X., Freeh, V.W.: Taming information-stealing smartphone applications (on android). In: McCune, J.M., Balacheff, B., Perrig, A., Sadeghi, A.-R., Sasse, A., Beres, Y. (eds.) Trust 2011. LNCS, vol. 6740, pp. 93–107. Springer, Heidelberg (2011)
Hao, H., Singh, V., Du, W.: On the effectiveness of api-level access control using bytecode rewriting in android. In: ACM SIGSAC Symposium on Information, Computer and Communications Security (2013)
Zhao, M., Ge, F., Zhang, T., Yuan, Z.: AntiMalDroid: An efficient SVM-based malware detection framework for android. In: Liu, C., Chang, J., Yang, A. (eds.) ICICA 2011, Part I. CCIS, vol. 243, pp. 158–166. Springer, Heidelberg (2011)
Enck, W., Gilbert, P., Chun, B.G., Cox, L.P., Jung, J., McDaniel, P., Sheth, A.: Taintdroid: An information-flow tracking system for realtime privacy monitoring on smartphones. In: OSDI (2010)
Kim, H., Smith, J., Shin, K.G.: Detecting energy-greedy anomalies and mobile malware variants. In: International Conference on Mobile Systems, Applications, and Services (2008)
Enck, W., Ongtang, M., McDaniel, P.: On lightweight mobile phone application certification. In: ACM Conference on Computer and Communications Security (2009)
Schmidt, A.D., Bye, R., Schmidt, H.G., Clausen, J., Kiraz, O., Yuksel, K.A., Camtepe, S.A., Albayrak, S.: Static analysis of executables for collaborative malware detection on android. In: ICC (2009)
Sahs, J., Khan, L.: A machine learning approach to android malware detection. In: Intelligence and Security Informatics Conference (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Grampurohit, V., Kumar, V., Rawat, S., Rawat, S. (2014). Category Based Malware Detection for Android. In: Mauri, J.L., Thampi, S.M., Rawat, D.B., Jin, D. (eds) Security in Computing and Communications. SSCC 2014. Communications in Computer and Information Science, vol 467. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44966-0_23
Download citation
DOI: https://doi.org/10.1007/978-3-662-44966-0_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-44965-3
Online ISBN: 978-3-662-44966-0
eBook Packages: Computer ScienceComputer Science (R0)