Packed malware variants detection using deep belief networks

[1]  Qinghua Zheng,et al.  Android Malware Familial Classification and Representative Sample Selection via Frequent Subgraph Analysis , 2018, IEEE Transactions on Information Forensics and Security.

[2]  Adam Doupé,et al.  Deep Android Malware Detection , 2017, CODASPY.

[3]  Zheng Qin,et al.  A feature-hybrid malware variants detection using CNN based opcode embedding and BPNN based API embedding , 2019, Comput. Secur..

[4]  Wanlei Zhou,et al.  Control Flow-Based Malware VariantDetection , 2014, IEEE Transactions on Dependable and Secure Computing.

[5]  Tao Xie,et al.  AppContext: Differentiating Malicious and Benign Mobile App Behaviors Using Context , 2015, 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering.

[6]  G. Sudha Sadasivam,et al.  Android Malware Detection , 2019, AISGSC 2019.

[7]  Peng Wang,et al.  AsDroid: detecting stealthy behaviors in Android applications by user interface and program behavior contradiction , 2014, ICSE.

[8]  Moshe Kam,et al.  System Call-Based Detection of Malicious Processes , 2015, 2015 IEEE International Conference on Software Quality, Reliability and Security.

[9]  Yuval Elovici,et al.  “Andromaly”: a behavioral malware detection framework for android devices , 2012, Journal of Intelligent Information Systems.

[10]  Zheng Qin,et al.  Dalvik Opcode Graph Based Android Malware Variants Detection Using Global Topology Features , 2018, IEEE Access.