DeepFlow: Deep learning-based malware detection by mining Android application for abnormal usage of sensitive data

Proceedings - IEEE Symposium on Computers and Communications

Volumn , Issue , 2017, Pages 438-443

  Zhu, Dali ^a   Jin, Hao ^a   Yang, Ying ^a   Wu, Di ^a   Chen, Weiyi ^b  

^a University of Chinese Academy of Sciences   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

Android malware detection; Characterization; Data flows; Deep learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; CHARACTERIZATION; COMPUTER CRIME; DATA TRANSFER; DEEP LEARNING; LEARNING ALGORITHMS; LEARNING SYSTEMS; MALWARE;

ABNORMAL BEHAVIOR; ANDROID APPLICATIONS; ANDROID MALWARE; APPLICATION DEVELOPERS; DATA FLOW; LEARNING TECHNIQUES; LEARNING-BASED APPROACH; MALWARE DETECTION;

ANDROID (OPERATING SYSTEM);

EID: 85030541939     PISSN: 15301346     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISCC.2017.8024568     Document Type: Conference Paper

References (21)

1. Arp, D., Gascon, H., Rieck, K., Spreitzenbarth, M., Hübner, M. : DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket. In: Network and Distributed System Security Symposium. NDSS (2014)
2. Arzt, S., Rasthofer, S., Fritz, C., Bodden, E., Bartel, A., Klein, J., McDaniel, P. : Flowdroid: Precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for android apps. ACM SIGPLAN Notices, 49 (6), 259-269 (2014)
3. Avdiienko, V., Kuznetsov, K., Gorla, A., Zeller, A., Arzt, S., Rasthofer, S., Bodden, E. : Mining apps for abnormal usage of sensitive data. In: 37th IEEE International Conference on Software Engineering, pp. 426-436. IEEE (2015)
4. Chen C. : Malicious code detection based on opcode behavior by deep learning. Doctoral dissertation, Harbin institute of technology (2013)
5. Deep Instinct, www. deepinstinct. com
6. Deep Learning, https: //en. wikipedia. org/wiki/Deep-learning
7. Enck, W., Gilbert, P., Han, S., Tendulkar, V., Chun, B. G., Cox, L. P., Sheth, A. N. : TaintDroid: An information-flow tracking system for realtime privacy monitoring on smartphones. ACM Transactions on Computer Systems (TOCS), 32 (2), (2014)
8. Gartner, Gartner says Android has surpassed 1. 6 billion shipments of devices, http: //www. gartn-er. com/newsroom/id/2954317, 2016
9. Hornyack, P., Han, S., Jung, J., Schechter, S., Wetherall, D. : These aren't the droids you're looking for: retrofitting android to protect data from imperious applications. In: Proceedings of the 18th ACM conference on Computer and communications security, pp. 639-652. ACM (2011)
10. LeCun, Y., Bengio, Y., Hinton, G. : Deep learning. Nature, 521 (7553), 436-444 (2015)
11. Narudin, F. A., Feizollah, A., Anuar, N. B., Gani, A. : Evaluation of machine learning classifiers for mobile malware detection. Soft Computing, 20 (1), 343-357 (2016)
12. Rasthofer, S., Arzt, S., Bodden, E. : A Machine-learning Approach for Classifying and Categorizing Android Sources and Sinks. In: Network and Distributed System Security Symposium. NDSS (2014)
13. Reps, T., Horwitz, S., Sagiv, M. : Precise interprocedural dataflow analysis via graph reachability. In: Proceedings of the 22nd ACM SIGPLAN-SIGACT symposium on Principles of programming languages, pp. 49-61. ACM (1995)
14. Sanz, B., Santos, I., Laorden, C., Ugarte-Pedrero, X., Bringas, P. G., Álvarez, G. : Puma: Permission usage to detect malware in android. In: International Joint Conference CISIS'12-ICEUTÉ12-SOCO12 Special Sessions, pp. 289-298. Springer, Berlin Heidelberg (2013)
15. Sensor Tower, https: //sensortower. com
16. Shabtai, A., Kanonov, U., Elovici, Y., Glezer, C., Weiss, Y. : "Andromaly": A behavioral malware detection framework for android devices. Journal of Intelligent Information Systems, 38 (1), 161-190 (2012)
17. VirusShare, http: //virusshare. com/
18. Wei, F., Roy, S., & Ou, X. : Amandroid: A precise and general intercomponent data flow analysis framework for security vetting of android apps. In: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, pp. 1329-1341. ACM (2014)
19. Wu, D. J., Mao, C. H., Wei, T. E., Lee, H. M., Wu, K. P. : Droidmat: Android malware detection through manifest and api calls tracing. In: Seventh Asia Joint Conference on Information Security (Asia JCIS), pp. 62-69. IEEE (2012)
20. Yuan, Z., Lu, Y., Xue, Y. : Droiddetector: Android malware characterization and detection using deep learning. Tsinghua Science and Technology, 21 (1), 114-123 (2016)
21. Zhou, Y., Jiang, X. : Dissecting android malware: Characterization and evolution. In: 2012 IEEE Symposium on Security and Privacy, pp. 95-109. IEEE (2012)