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Experimental Analysis on Big Data in IOT-Based Architecture Read chapter Read first chapter

2017 | OriginalPaper | Chapter

NeSeDroid—Android Malware Detection Based on Network Traffic and Sensitive Resource Accessing

Authors : Nguyen Tan Cam, Nguyen Cam Hong Phuoc

Published in: Proceedings of the International Conference on Data Engineering and Communication Technology

Publisher: Springer Singapore

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Abstract

The Android operating system has a large market share. The number of new malware on Android is increasing much recently. Android malware analysis includes static analysis and dynamic analysis. Limitations of static analysis are the difficulty in analyzing the malware using encryption techniques, to confuse the source, and to change behavior itself. In this paper, we proposed a hybrid analysis method, named NeSeDroid. This method used static analysis to detect the sensitive resource accessing. It also used dynamic analysis to detect sensitive resource leakage, through Internet connection. The method is tested on the list of applications which are downloaded from Android Apps Market, Genome Malware Project dataset and our additional samples in DroidBench dataset. The evaluation results show that the NeSeDroid has the high accuracy and it reduces the rate of fail positive detection.

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Literature
1.
2.
3.
Sanz, B., et al., MAMA: Manifest Analysis For Malware Detection In Android. Cybern. Syst., pp. 469–488 (2013).
4.
Moonsamy, V., et al., Contrasting Permission Patterns between Clean and Malicious Android Applications, in Security and Privacy in Communication Networks, T. Zia, et al., Springer International Publishing. pp. 69–85 (2013).
5.
Gascon, H., et al., Structural detection of android malware using embedded call graphs, in Proceedings of the 2013 ACM workshop on Artificial intelligence and security. ACM: Berlin, Germany. pp. 45–54 (2013).
6.
Li, L., et al., IccTA: Detecting Inter-Component Privacy Leaks in Android Apps, in The 37th International Conference on Software Engineering (ICSE). Firenze, Italy (2015).
7.
Li, L., et al. Automatically Exploiting Potential Component Leaks in Android Applications. in Trust, Security and Privacy in Computing and Communications (TrustCom), 2014 IEEE 13th International Conference on. (2014).
8.
Aafer, Y., W. Du, and H. Yin, DroidAPIMiner: Mining API-Level Features for Robust Malware Detection in Android, in Security and Privacy in Communication Networks, T. Zia, et al., Springer International Publishing. pp. 86–103 (2013).
9.
Arzt, S., et al., FlowDroid: precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for Android apps, in Proceedings of the 35th ACM SIGPLAN Conference on Programming Language Design and Implementation. ACM: Edinburgh, United Kingdom. pp. 259–269 (2014).
10.
Bagheri, H., et al., COVERT: Compositional Analysis of Android Inter-App Permission Leakage. Software Engineering, IEEE Transactions on, pp. 1–1 (2015).
11.
Dini, G., et al., MADAM: a multi-level anomaly detector for android malware, in Proceedings of the 6th international conference on Mathematical Methods, Models and Architectures for Computer Network Security: computer network security. Springer-Verlag: St. Petersburg, Russia. pp. 240–253 (2012).
12.
Shabtai, A., et al., “Andromaly”: a behavioral malware detection framework for android devices. J. Intell. Inf. Syst., pp. 161–190 (2012).
13.
Zheng, C., et al., SmartDroid: an automatic system for revealing UI-based trigger conditions in android applications, in Proceedings of the second ACM workshop on Security and privacy in smartphones and mobile devices. ACM: Raleigh, North Carolina, USA. pp. 93–104 (2012).
14.
Enck, W., et al., TaintDroid: an information-flow tracking system for realtime privacy monitoring on smartphones, in Proceedings of the 9th USENIX conference on Operating systems design and implementation. USENIX Association: Vancouver, BC, Canada. pp. 1–6 (2010).
15.
Zaman, M., et al. Malware detection in Android by network traffic analysis. in Networking Systems and Security (NSysS) (2015).
16.
Wu, X., et al., Detect repackaged Android application based on HTTP traffic similarity. Security and Communication Networks, (2015).
17.
Zheng, M., M. Sun, and J.C.S. Lui, DroidRay: a security evaluation system for customized android firmwares, in Proceedings of the 9th ACM symposium on Information, computer and communications security. ACM: Kyoto, Japan. pp. 471–482 (2014).
18.
Feldman, S., D. Stadther, and W. Bing. Manilyzer: Automated Android Malware Detection through Manifest Analysis. in Mobile Ad Hoc and Sensor Systems (MASS), IEEE 11th International Conference on. (2014).
19.
20.
Feizollah, A., et al. Comparative study of k-means and mini batch k-means clustering algorithms in android malware detection using network traffic analysis. in Biometrics and Security Technologies (ISBAST), 2014 International Symposium on. (2014).
21.
Jun, L., et al. Research of android malware detection based on network traffic monitoring. in Industrial Electronics and Applications (ICIEA), 2014 IEEE 9th Conference on. (2014).
22.
Shabtai, A., et al., Mobile malware detection through analysis of deviations in application network behavior. Computers & Security, pp. 1–18 (2014).
23.
24.
25.
Rasthofer, S., S. Arzt, and E. Bodden, A Machine-learning Approach for Classifying and Categorizing Android Sources and Sinks. (2014).
26.
27.
28.
29.
30.
Metadata
Title
NeSeDroid—Android Malware Detection Based on Network Traffic and Sensitive Resource Accessing
Authors
Nguyen Tan Cam
Nguyen Cam Hong Phuoc
Copyright Year
2017
Publisher
Springer Singapore
Book
Proceedings of the International Conference on Data Engineering and Communication Technology

Print ISBN: 978-981-10-1677-6

Electronic ISBN: 978-981-10-1678-3

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-981-10-1678-3_3

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