[1] |
ZHANG Jinglian,PENG Yanbing.Research on malware code classification based on features fusion[J].Computer Engineering,2019,45(8):281-286,295.(in Chinese)张景莲,彭艳兵.基于特征融合的恶意代码分类研究[J].计算机工程,2019,45(8):281-286,295.
|
[2] |
ZHOU Zizhan,WANG Junfeng.Research on feature extraction of malware bytecode based on GPU acceleration[J].Journal of Sichuan University(Natural Science Edition),2019,56(2):45-52.(in Chinese)周紫瞻,王俊峰.基于GPU加速的恶意代码字节码特征提取方法研究[J].四川大学学报(自然科学版),2019,56(2):45-52.
|
[3] |
IMRAN M,AFZAL M T,QADIR M A.Similarity-based malware classification using hidden Markov model[C]//Proceedings of the 4th International Conference on Cyber Security,Cyber Warfare,and Digital Forensic(CyberSec).Washington D.C.,USA:IEEE Press,2015:129-134.
|
[4] |
MACÍAS M,BARRÍA C,ACUNA A,et al.SGSI support throught malware's classification using a pattern analysis[C]//Proceedings of 2016 IEEE International Conference on Automatica.Washington D.C.,USA:IEEE Press,2016:1-4.
|
[5] |
SALEHI Z,GHIASI M,SAMI A.A miner for malware detection based on API function calls and their arguments[C]//Proceedings of the 16th CSI International Symposium on Artificial Intelligence and Signal Processing.Washington D.C.,USA:IEEE Press,2012:563-568.
|
[6] |
ALAM S,TRAORE I,SOGUKPINAR I.Annotated control flow graph for metamorphic malware detection[J].The Computer Journal,2015,58(10):2608-2621.
|
[7] |
KONO K,PHOMKEONA S,OKAMURA K.An unknown malware detection using execution registry access[C]//Proceedings of the 42nd Annual Computer Software and Applications Conference.Washington D.C.,USA:IEEE Press,2018:487-491.
|
[8] |
LIU Yashu,WANG Zhihai,HOU Yueran,et al.Malware visualization and automatic classification with enhanced information density[J].Journal of Tsinghua University(Science and Technology),2019,59(1):9-14.(in Chinese)刘亚姝,王志海,侯跃然,等.信息密度增强的恶意代码可视化与自动分类方法[J].清华大学学报(自然科学版),2019,59(1):9-14.
|
[9] |
LIU Yashu,LAI Yukun,WANG Zhihai,et al.A new learning approach to malware classification using discriminative feature extraction[J].IEEE Access,2019,7:13015-13023.
|
[10] |
HAN Xiaoguang,QU Wu,YAO Xuanxia,et al.Research on malicious code variants detection based on texture fingerprint[J].Journal of Communications,2014,35(8):125-136.(in Chinese)韩晓光,曲武,姚宣霞,等,基于纹理指纹的恶意代码变种检测方法研究[J].通信学报,2014,35(8):125-136.
|
[11] |
KUMARI M,HSIEH G,OKONKWOC A.Deep learning approach to malware multi-class classification using image processing techniques[C]//Proceedings of 2017 International Conference on Computational Science and Computational Intelligence.Washington D.C.,USA:IEEE Press,2017:13-18.
|
[12] |
WANG Tingting,XU Ning.Malware variants detection based on opcode image recognition in small training set[C]//Proceedings of 2017 IEEE 2nd International Conference on Cloud Computing and Big Data Analysis.Washington D.C.,USA:IEEE Press,2017:328-332.
|
[13] |
REN Zhuojun,CHEN Guang.Application of entropy visualization method in malware classification[J].Computer Engineering,2017,43(9):167-171.(in Chinese)任卓君,陈光.熵可视化方法在恶意代码分类中的应用[J].计算机工程,2017,43(9):167-171.
|
[14] |
FU Jianwen,XUE Jingfeng,WANG Yong,et al.Malware visualization for fine-grained classification[J].IEEE Access,2018,6:14510-14523.
|
[15] |
DAI Yihui,YIN Xudong.Malicious code detection based on random forest[J].Cyberspace Security,2018,9(2):70-75.(in Chinese)戴逸辉,殷旭东.基于随机森林的恶意代码检测[J].网络空间安全,2018,9(2):70-75.
|
[16] |
KHAN R U,ZHANG X,KUMAR R.Analysis of ResNet and GoogleNet models for malware detection[J].Journal of Computer Virology and Hacking Techniques,2019,15(1):29-37.
|
[17] |
KIM H J.Image-based malware classification using convolutional neural network[M]//PARK J J,LOIA V,YI G,et al.Advances in computer science and ubiquitous computing.Berlin,Germany:Springer,2017:1352-1357.
|
[18] |
NI Sang,QIAN Quan,ZHANG Rui.Malware identification using visualization images and deep learning[J].Computers & Security,2018,77(6):871-885.
|
[19] |
CUI Zhihua,XUE Fei,CAI Xingjuan,et al.Detection of malicious code variants based on deep learning[J].IEEE Transactions on Industrial Informatics,2018,14(7):3187-3196.
|
[20] |
LUO Shiqi.Research on malware analysis and detection based on deep learning[D].Urumqi:Xinjiang University,2018.(in Chinese)罗世奇.深度学习的恶意代码分析与检测技术研究[D].乌鲁木齐:新疆大学,2018.
|
[21] |
NATARAJ L,KARTHIKEYAN S,JACOB G,et al.Malware images:visualization and automatic classification[C]//Proceedings of the 8th International Symposium on Visualization for Cyber Security.New York,USA:ACM Press,2011:1-7.
|
[22] |
SANDLER M,HOWARD A,ZHU M,et al.Mobilenet v2:inverted residuals and linear bottlenecks[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2018:4510-4520.
|
[23] |
HOWARD A G,ZHU M,CHEN B,et al.MobileNets:efficient convolutional neural networks for mobile vision applications[EB/OL].[2019-03-01].https://arxiv.xilesou.top/abs/1704.04861.
|
[24] |
KALASH M,ROCHAN M,MOHAMMEDN,et al.Malware classification with deep convolutional neural networks[C]//Proceedings of the 9th IFIP International Conference on New Technologies,Mobility and Security.Washington D.C.,USA:IEEE Press,2018:1-5.
|
[25] |
YANG Chun,WEN Yu,GUO Jianbin,et al.A convolutional neural network based classifier for uncompressed malware samples[C]//Proceedings of the 1st Workshop on Security-Oriented Designs of Computer Architectures and Processors.New York,USA:ACM Press,2018:15-17.
|