Malware Detection Based on Deep Learning of Behavior Graphs
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Yi Sun | Zhaowen Lin | Fei Xiao | Yan Ma | Yan Ma | Zhaowen Lin | Fei Xiao | Yi Sun
[1] Lionel C. Briand,et al. A scalable approach for malware detection through bounded feature space behavior modeling , 2013, 2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE).
[2] Christopher Krügel,et al. A quantitative study of accuracy in system call-based malware detection , 2012, ISSTA 2012.
[3] Qinghua Zheng,et al. Android Malware Familial Classification and Representative Sample Selection via Frequent Subgraph Analysis , 2018, IEEE Transactions on Information Forensics and Security.
[4] Wei Zhang,et al. Semantics-Based Online Malware Detection: Towards Efficient Real-Time Protection Against Malware , 2016, IEEE Transactions on Information Forensics and Security.
[5] Qing Liu,et al. Down image recognition based on deep convolutional neural network , 2018, Information Processing in Agriculture.
[6] Jin Kwak,et al. Automatic malware mutant detection and group classification based on the n-gram and clustering coefficient , 2015, The Journal of Supercomputing.
[7] Razvan Pascanu,et al. Malware classification with recurrent networks , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[8] Nikolaos Doulamis,et al. Stacked Autoencoders for Outlier Detection in Over-the-Horizon Radar Signals , 2017, Comput. Intell. Neurosci..
[9] Rajkumar Buyya,et al. CloudEyes: Cloud‐based malware detection with reversible sketch for resource‐constrained internet of things (IoT) devices , 2017, Softw. Pract. Exp..
[10] Lwin Khin Shar,et al. Scalable malware clustering through coarse-grained behavior modeling , 2012, SIGSOFT FSE.
[11] B. Aditya Prakash,et al. Graphs for Malware Detection : The Next Frontier , 2017 .
[12] Deepti Vidyarthi,et al. Malware Detection Using API Function Frequency with Ensemble Based Classifier , 2013, SSCC.
[13] Bazara I. A. Barry,et al. Improving the Detection of Malware Behaviour Using Simplified Data Dependent API Call Graph , 2013 .
[14] Claudia Eckert,et al. Deep Learning for Classification of Malware System Call Sequences , 2016, Australasian Conference on Artificial Intelligence.
[15] Engin Kirda,et al. UNVEIL: A large-scale, automated approach to detecting ransomware (keynote) , 2016, SANER.
[16] Yudong Zhang,et al. Binary PSO with mutation operator for feature selection using decision tree applied to spam detection , 2014, Knowl. Based Syst..
[17] Christopher Krügel,et al. Scalable, Behavior-Based Malware Clustering , 2009, NDSS.
[18] Lida Xu,et al. The internet of things: a survey , 2014, Information Systems Frontiers.
[19] Kaiming He,et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[20] Jemal H. Abawajy,et al. Malware Threats and Detection for Industrial Mobile-IoT Networks , 2018, IEEE Access.
[21] Luigi Alfredo Grieco,et al. Security, privacy and trust in Internet of Things: The road ahead , 2015, Comput. Networks.
[22] Wanlei Zhou,et al. Control Flow-Based Malware VariantDetection , 2014, IEEE Transactions on Dependable and Secure Computing.
[23] Sachchidanand Singh,et al. Internet of Things (IoT): Security challenges, business opportunities & reference architecture for E-commerce , 2015, 2015 International Conference on Green Computing and Internet of Things (ICGCIoT).
[24] Youssef B. Mahdy,et al. Behavior-based features model for malware detection , 2016, Journal of Computer Virology and Hacking Techniques.
[25] Zhenlong Yuan,et al. DroidDetector: Android Malware Characterization and Detection Using Deep Learning , 2016 .
[26] Yanfang Ye,et al. DL 4 MD : A Deep Learning Framework for Intelligent Malware Detection , 2016 .
[27] Lukáš Vařeka,et al. Stacked Autoencoders for the P300 Component Detection , 2017, Front. Neurosci..
[28] Nathan S. Netanyahu,et al. DeepSign: Deep learning for automatic malware signature generation and classification , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).
[29] Wenyi Huang,et al. MtNet: A Multi-Task Neural Network for Dynamic Malware Classification , 2016, DIMVA.
[30] Antonio Iera,et al. The Internet of Things: A survey , 2010, Comput. Networks.
[31] Jun Yu,et al. Coupled Deep Autoencoder for Single Image Super-Resolution , 2017, IEEE Transactions on Cybernetics.
[32] Kouichi Sakurai,et al. Lightweight Classification of IoT Malware Based on Image Recognition , 2018, 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC).
[33] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[34] Nikolaos Doulamis,et al. Deep Learning for Computer Vision: A Brief Review , 2018, Comput. Intell. Neurosci..
[35] Wu Liu,et al. Behavior-Based Malware Analysis and Detection , 2011, 2011 First International Workshop on Complexity and Data Mining.
[36] Ning Zhang,et al. Efficient Signature Generation for Classifying Cross-Architecture IoT Malware , 2018, 2018 IEEE Conference on Communications and Network Security (CNS).
[37] Dong Xiang,et al. Information-theoretic measures for anomaly detection , 2001, Proceedings 2001 IEEE Symposium on Security and Privacy. S&P 2001.
[38] Christopher Krügel,et al. Effective and Efficient Malware Detection at the End Host , 2009, USENIX Security Symposium.
[39] Bazara I. A. Barry,et al. Enhancing the Detection of Metamorphic Malware using Call Graphs , 2015 .
[40] Eunjin Kim,et al. A Novel Approach to Detect Malware Based on API Call Sequence Analysis , 2015, Int. J. Distributed Sens. Networks.
[41] Jun Yu,et al. Multitask Autoencoder Model for Recovering Human Poses , 2018, IEEE Transactions on Industrial Electronics.