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International Journal of Information Security
Erschienen in: International Journal of Information Security 2/2015

01.04.2015 | Special Issue Paper

Mobile-Sandbox: combining static and dynamic analysis with machine-learning techniques

verfasst von: Michael Spreitzenbarth, Thomas Schreck, Florian Echtler, Daniel Arp, Johannes Hoffmann

Erschienen in: International Journal of Information Security | Ausgabe 2/2015

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Abstract

Smartphones in general and Android in particular are increasingly shifting into the focus of cyber criminals. For understanding the threat to security and privacy, it is important for security researchers to analyze malicious software written for these systems. The exploding number of Android malware calls for automation in the analysis. In this paper, we present Mobile-Sandbox, a system designed to automatically analyze Android applications in novel ways: First, it combines static and dynamic analysis, i.e., results of static analysis are used to guide dynamic analysis and extend coverage of executed code. Additionally, it uses specific techniques to log calls to native (i.e., “non-Java”) APIs, and last but not least it combines these results with machine-learning techniques to cluster the analyzed samples into benign and malicious ones. We evaluated the system on more than 69,000 applications from Asian third-party mobile markets and found that about 21 % of them actually use native calls in their code.
Vorheriger Artikel Formal modeling and automatic enforcement of Bring Your Own Device policies
Nächster Artikel Lightweight energy consumption-based intrusion detection system for wireless sensor networks

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Literatur
1.
Aafer, Y., Du, W., Yin, H.: DroidAPIMiner: Mining API-level features for robust malware detection in android. In: Proc. of International Conference on Security and Privacy in Communication Networks (SecureComm) (2013)
2.
3.
4.
Arp, D., Spreitzenbarth, M., Hübner, M., Gascon, H., Rieck, K.: Drebin: Efficient and explainable detection of android malware in your pocket. In: Proc. of Network and Distributed System Security Symposium (NDSS) (2014)
5.
Bläsing, T., Batyuk, L., Schmidt, A.-D., Camtepe, S., Albayrak, S.: An android application sandbox system for suspicious software detection. In: Proc. of the 5th International Conference on Malicious and Unwanted Software (MALWARE) (2010)
6.
Burguera, I., Zurutuza, U., Nadjm-Tehrani, S.: Crowdroid: behavior-based malware detection system for android. In: Proc. of the 1st ACM Workshop on Security and Privacy in Smartphones and Mobile Devices (2011)
7.
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)
8.
9.
10.
Desnos, A., Gueguen, G.: Android: From reversing to decompilation. In: Proc. of Black Hat Abu Dhabi (2011)
11.
12.
Enck, W., Gilbert, P., gon Chun, B., Cox, L. P., Jung, J., McDaniel, P., Sheth, A.N.: TaintDroid: an information-flow tracking system for realtime privacy monitoring on smartphones. In: Proc. of the USENIX Symposium on Operating Systems Design and Implementation (OSDI), October 2010
13.
Felt, A., Finifter, M., Chin, E., Hanna, S., Wagner, D.: A survey of mobile malware in the wild. In: Proceedings of the 1st ACM wWorkshop on Security and Privacy in Smartphones and Mobile Devices, pp. 3–14. ACM (2011)
14.
Felt, A.P., Chin, E., Hanna, S., Song, D., Wagner, D.: Android permissions demystified. In: Proc. of the 18th ACM Conference on Computer and Communications Security (2011)
15.
16.
17.
Groves, R.M.: Research on survey data quality. Public Opin. Q. 51(2), 157–172 (1987)
18.
19.
20.
21.
22.
Moser, A., Kruegel, C., Kirda, E.: Limits of static analysis for malware detection. In: Proc. of the 23rd Annual Computer Security Applications Conference (2007)
23.
Peng, H., Gates, C.S., Sarma, B.P., Li, N., Qi, Y., Potharaju, R., Nita-Rotaru, C., Molloy, I.: Using probabilistic generative models for ranking risks of android apps. pp. 241–252 (2012)
24.
Raffetseder, T., Kruegel, C., Kirda, E.: Detecting system emulators. In: ISC, pp. 1–18 (2007)
25.
26.
Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975)CrossRefMATH
27.
Sarma, B.P., Li, N., Gates, C., Potharaju, R., Nita-Rotaru, C., Molloy, I.: Android permissions: a perspective combining risks and benefits. In: Proc. of ACM Symposium on Access Control Models and Technologies (SACMAT), pp. 13–22 (2012)
28.
Schmidt, A.-D., Bye, R., Schmidt, H.-G., Clausen, J., Kiraz, O., Yüksel, K., Camtepe, S., Sahin, A.: Static analysis of executables for collaborative malware detection on android. In: Proc. of the ICC Communication and Information Systems Security Symposium (2009)
29.
Six, J.: Application Security for the Android Platform: Processes, Permissions, and Other Safeguards. Oreilly & Assoc Inc, Sebastopol (2011)
30.
31.
Spreitzenbarth, M., Freiling, F.C.: Android Malware on the Rise. Technical Report CS-2012-04, Dept. of Computer Science, University of Erlangen-Nuremberg, April 2012
32.
33.
34.
Willems, C., Freiling, F.C.: Reverse code engineering—state of the art and countermeasures. it-Information Technology, pp. 53–63 (2011)
35.
Willems, C., Holz, T., Freiling, F.C.: Toward automated dynamic malware analysis using CWSandbox. IEEE Secur. Priv. 5(2), 32–39 (2007)CrossRef
36.
Xie, L., Zhang, X., Seifert, J.-P., Zhu, S.: pbmds: a behavior-based malware detection system for cellphone devices. In: Proc. of the Third ACM Conference on Wireless Network Security (2010)
37.
Zhou, Y., Jiang, X.: Dissecting android malware: characterization and evolution. In: Proc. of the 33rd IEEE Symposium on Security and Privacy (Oakland 2012), May 2012
38.
Zhou, Y., Wang, Z., Zhou, W., Jiang, X.: Hey, you, get off of my market: detecting malicious Apps in official and alternative Android markets. In: Proc. of the 19th Annual Symposium on Network and Distributed System Security (2012)
Metadaten
Titel
Mobile-Sandbox: combining static and dynamic analysis with machine-learning techniques
verfasst von
Michael Spreitzenbarth
Thomas Schreck
Florian Echtler
Daniel Arp
Johannes Hoffmann
Publikationsdatum
01.04.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Information Security / Ausgabe 2/2015
Print ISSN: 1615-5262
Elektronische ISSN: 1615-5270
DOI
https://doi.org/10.1007/s10207-014-0250-0

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