DroidDeep: using Deep Belief Network to characterize and detect android malware

Author:

Su Xin,Shi Weiqi,Qu Xilong,Zheng Yi,Liu Xuchong

Funder

Research Foundation of Education Bureau of Hunan Province

Science and Technology Projects of Hunan Province

Science and Technology Project of Hunan Province of China

Research project of Education Department of Hunan Province

Open Research Fund of Hunan Provincial Key Laboratory of Network Investigational Technology

key project of educational planning of Hunan Province

Publisher

Springer Science and Business Media LLC

Subject

Geometry and Topology,Theoretical Computer Science,Software

Reference43 articles.

1. Aafer Y, Du WL, Yin H (2018) Droidapiminer: Mining api-level features for robust malware detection in android. In: International conference on security and privacy in communication systems (Securecomm), pp 86–103

2. Apvrille L, Apvrille A (2015) Identifying unknown android malware with feature extractions and classification techniques. In: Conference on trust, security and privacy in computing and communications (Trustcom), pp 66–76

3. Arshad S, Shah A, Wahid A, Mehmood A, Song H (2018) SAMADroid: a novel 3-level hybrid malware detection model for android operating system. IEEE Access 6:4321–4339

4. Au KWY, Zhou Y, Huang Z (2012) Pscout: analyzing the android permission specification. In: Proceedings of the 2012 ACM conference on Computer and communications security. ACM, pp 217–228

5. Bengio Y (2009) Learning deep architectures for AI. Found Trends Mach Learn 2(1):1–127

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