A feature-hybrid malware variants detection using CNN based opcode embedding and BPNN based API embedding
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Zheng Qin | Kehuan Zhang | Jixin Zhang | Lu Ou | Hui Yin | Kehuan Zhang | Jixin Zhang | Zheng Qin | H. Yin | L. Ou
[1] Sung-Bae Cho,et al. Zero-day malware detection using transferred generative adversarial networks based on deep autoencoders , 2018, Inf. Sci..
[2] Xiangliang Zhang,et al. Exploring Permission-Induced Risk in Android Applications for Malicious Application Detection , 2014, IEEE Transactions on Information Forensics and Security.
[3] Phil Blunsom,et al. A Convolutional Neural Network for Modelling Sentences , 2014, ACL.
[4] Duen Horng Chau,et al. Guilt by association: large scale malware detection by mining file-relation graphs , 2014, KDD.
[5] Peng Wang,et al. AsDroid: detecting stealthy behaviors in Android applications by user interface and program behavior contradiction , 2014, ICSE.
[6] H. Hotelling. Analysis of a complex of statistical variables into principal components. , 1933 .
[7] Gianluca Stringhini,et al. Marmite: Spreading Malicious File Reputation Through Download Graphs , 2017, ACSAC.
[8] Adam Doupé,et al. Deep Android Malware Detection , 2017, CODASPY.
[9] Barbara G. Ryder,et al. Detection of Repackaged Android Malware with Code-Heterogeneity Features , 2020, IEEE Transactions on Dependable and Secure Computing.
[10] Sencun Zhu,et al. Privacy Risk Analysis and Mitigation of Analytics Libraries in the Android Ecosystem , 2020, IEEE Transactions on Mobile Computing.
[11] Heng Yin,et al. DroidScope: Seamlessly Reconstructing the OS and Dalvik Semantic Views for Dynamic Android Malware Analysis , 2012, USENIX Security Symposium.
[12] Yong Qi,et al. Detecting Malware with an Ensemble Method Based on Deep Neural Network , 2018, Secur. Commun. Networks.
[13] J. Borwein,et al. Two-Point Step Size Gradient Methods , 1988 .
[14] Zheng Qin,et al. IRMD: Malware Variant Detection Using Opcode Image Recognition , 2016, 2016 IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS).
[15] Xiangliang Zhang,et al. Detecting Android malicious apps and categorizing benign apps with ensemble of classifiers , 2018, Future Gener. Comput. Syst..
[16] Yidong Li,et al. DroidEnsemble: Detecting Android Malicious Applications With Ensemble of String and Structural Static Features , 2018, IEEE Access.
[17] Moshe Kam,et al. System Call-Based Detection of Malicious Processes , 2015, 2015 IEEE International Conference on Software Quality, Reliability and Security.
[18] Farnam Jahanian,et al. CloudAV: N-Version Antivirus in the Network Cloud , 2008, USENIX Security Symposium.
[19] Igor Santos,et al. Opcode sequences as representation of executables for data-mining-based unknown malware detection , 2013, Inf. Sci..
[20] Yuval Elovici,et al. “Andromaly”: a behavioral malware detection framework for android devices , 2012, Journal of Intelligent Information Systems.
[21] Zheng Qin,et al. Dalvik Opcode Graph Based Android Malware Variants Detection Using Global Topology Features , 2018, IEEE Access.
[22] Borja Sanz,et al. Using Dalvik opcodes for malware detection on android , 2015, Log. J. IGPL.
[23] Byung-Gon Chun,et al. TaintDroid: An Information-Flow Tracking System for Realtime Privacy Monitoring on Smartphones , 2010, OSDI.
[24] Christopher Krügel,et al. Effective and Efficient Malware Detection at the End Host , 2009, USENIX Security Symposium.
[25] Carsten Willems,et al. Automatic analysis of malware behavior using machine learning , 2011, J. Comput. Secur..
[26] Simin Nadjm-Tehrani,et al. Crowdroid: behavior-based malware detection system for Android , 2011, SPSM '11.
[27] Sakir Sezer,et al. N-opcode analysis for android malware classification and categorization , 2016, 2016 International Conference On Cyber Security And Protection Of Digital Services (Cyber Security).
[28] Jon Barker,et al. Malware Detection by Eating a Whole EXE , 2017, AAAI Workshops.
[29] Vinod Yegneswaran,et al. A comparative assessment of malware classification using binary texture analysis and dynamic analysis , 2011, AISec '11.
[30] Wanlei Zhou,et al. Control Flow-Based Malware VariantDetection , 2014, IEEE Transactions on Dependable and Secure Computing.
[31] Igor Santos,et al. OPEM: A Static-Dynamic Approach for Machine-Learning-Based Malware Detection , 2012, CISIS/ICEUTE/SOCO Special Sessions.
[32] Zheng Qin,et al. Malware Variant Detection Using Opcode Image Recognition with Small Training Sets , 2016, 2016 25th International Conference on Computer Communication and Networks (ICCCN).
[33] Sukumar Nandi,et al. Obfuscated malware detection using API call dependency , 2012, SecurIT '12.
[34] Jian Liu,et al. Understanding Android Obfuscation Techniques: A Large-Scale Investigation in the Wild , 2018, SecureComm.
[35] Xudong Ma,et al. Dynamic Android Malware Classification Using Graph-Based Representations , 2016, 2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud).
[36] Yanfang Ye,et al. Analyzing File-to-File Relation Network in Malware Detection , 2015, WISE.
[37] David Camacho,et al. MOCDroid: multi-objective evolutionary classifier for Android malware detection , 2017, Soft Comput..
[38] Roberto Baldoni,et al. Android malware family classification based on resource consumption over time , 2017, 2017 12th International Conference on Malicious and Unwanted Software (MALWARE).
[39] Qinghua Zheng,et al. Android Malware Familial Classification and Representative Sample Selection via Frequent Subgraph Analysis , 2018, IEEE Transactions on Information Forensics and Security.
[40] Zheng Qin,et al. Sensitive system calls based packed malware variants detection using principal component initialized MultiLayers neural networks , 2018, Cybersecur..
[41] Wenke Lee,et al. PolyUnpack: Automating the Hidden-Code Extraction of Unpack-Executing Malware , 2006, 2006 22nd Annual Computer Security Applications Conference (ACSAC'06).