[1] MAHENDRAN A,VEDALDI A.Understanding deep image representations by inverting them[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2015:5188-5196. [2] DENG L.Deep learning:methods and applications[J].Foundations and Trends in Signal Processing,2014,7(3/4):197-387. [3] VISHWANATHAN S V M,MURTY M N.SSVM:a simple SVM algorithm[C]//Proceedings of 2002 International Joint Conference on Neural Networks.Washington D.C.,USA:IEEE Press,2002:2393-2398. [4] ZHANG M L,ZHOU Z H.ML-KNN:a lazy learning approach to multi-label learning[J].Pattern Recognition,2007,40(7):2038-2048. [5] SAFAVIAN S R,LANDGREBE D.A survey of decision tree classifier methodology[J].IEEE Transactions on Systems,Man,and Cybernetics,1991,21(3):660-674. [6] LIAW A,WIENER M.Classification and regression by random forest[J].R News,2002,2(3):18-22. [7] SAXE J,BERLIN K.Deep neural network based malware detection using two dimensional binary program features[C]//Proceedings of 2015 International Conference on Malicious and Unwanted Software.Washington D.C.,USA:IEEE Press,2015:11-20. [8] GROSSE K,PAPERNOT N,MANOHARAN P,et al.Adversarial examples for malware detection[C]//Proceedings of European Symposium on Research in Computer Security.Berlin,Germany:Springer,2017:62-79. [9] TOBIYAMA S,YAMAGUCHI Y,SHIMADA H,et al.Malware detection with deep neural network using process behavior[C]//Proceedings of 2016 IEEE Annual Computer Software and Applications Conference.Washington D.C.,USA:IEEE Press,2016:577-582. [10] TIAN R,ISLAM R,BATTEN L,et al.Differentiating malware from cleanware using behavioural analysis[C]//Proceedings of 2010 International Conference on Malicious and Unwanted Software.Washington D.C.,USA:IEEE Press,2010:23-30. [11] COZZI E,GRAZIANO M,FRATANTONIO Y,et al.Understanding Linux malware[EB/OL].[2019-04-20].http://www.s3.eurecom.fr/docs/oakland18_cozzi.pdf. [12] MARCOS S,RIVERA R,KOTZIAS P,et al.AVclass:a tool for massive malware labeling[C]//Proceedings of International Symposium on Research in Attacks,Intrusions,and Defenses.Berlin,Germany:Springer,2016:263-276. [13] WANG Xiajing,HU Changzhen,MA Rui,et al.A survey of the key technology of binary program vulnerability discovery[J].Netinfo Security,2017(8):1-13.(in Chinese)王夏菁,胡昌振,马锐,等.二进制程序漏洞挖掘关键技术研究综述[J].信息网络安全,2017(8):1-13. [14] Systemtap[EB/OL].[2019-04-20].https://sourceware.org/systemtap. [15] WU Di,FANG Binxing,CUI Xiang,et al.BotCatcher:botnet detection system based on deep learning[J].Journal on Communications,2018,39(8):18-28.(in Chinese)吴迪,方滨兴,崔翔,等.BotCatcher:基于深度学习的僵尸网络检测系统[J].通信学报,2018,39(8):18-28. [16] WANG Guihuai,ZHONG Cheng,CHU Xiumin,et al.Ship trajectory restoration method based on BLSTM-RNN[J].Journal of Chongqing Jiaotong University(Natural Sciences),2019,38(10):7-12,67.(in Chinese)王贵槐,钟诚,初秀民,等.基于BLSTM-RNN的船舶轨迹修复方法[J].重庆交通大学学报(自然科学版),2019,38(10):7-12,67. [17] RUI F,ZUO Z,LI L.Using LSTM and GRU neural network methods for traffic flow prediction[C]//Proceedings of Youth Academic Conference of Chinese Association of Automation.Washington D.C.,USA:IEEE Press,2017:266-276. [18] RHODE M,BURNAP P,JONES K.Early-stage malware prediction using recurrent neural networks[J].Computers & Security,2018,77:578-594. [19] NATHANLOPE Z.Python remote administration tool[EB/OL].[2019-04-20].https://github.com/nathanlopez/Stitch. [20] HUANG Wenming,MO Yang.Chinese spam message filtering based on text weighted KNN algorithm[J].Computer Engineering,2017,43(3):193-199.(in Chinese)黄文明,莫阳.基于文本加权KNN算法的中文垃圾短信过滤[J].计算机工程,2017,43(3):193-199. [21] FANG Yong,ZHU Guangxiatian,LIU Luping,et al.Research on browser Fuzz sample generation technology based on deep learning[J].Netinfo Security,2019,19(3):26-33.(in Chinese)方勇,朱光夏天,刘露平,等.基于深度学习的浏览器Fuzz样本生成技术研究[J].信息网络安全,2019,19(3):26-33. |