计算机科学 ›› 2019, Vol. 46 ›› Issue (7): 86-90.doi: 10.11896/j.issn.1002-137X.2019.07.013
王乐乐1,汪斌强1,刘建港2,张建辉1,苗启广3
WANG Le-le1,WANG Bin-qiang1,LIU Jian-gang2,ZHANG Jian-hui1,MIAO Qi-guang3
摘要： 针对传统恶意程序检测判定效率低及自动分析恶意程序能力不足的问题,在深度学习环境下,研究利用递归神经网络进行恶意程序的检测分类的问题。首先,用快速模拟器(Quick Emulator,QEMU)捕获到恶意程序运行时所调用的API及其参数序列,经过行为抽象,形成恶意程序的特征序列。然后使用对数化的双线性模型(Hierarchical Log-bilinear Language Model,HLBL)将特征序列映射成固定长度的词向量,并将这些词向量合成递归神经网络(Recursive Neural Network,RNN)所需要的输入矩阵。通过对递归神经网络模型的训练,建立恶意程序的多层语义聚合模型,完成对恶意程序的分类检测。实验数据表明,递归神经网络模型在恶意程序检测分类中能够有效地检测出恶意程序,与传统机器学习算法相比,其检测率提高了17%。特别是在引入张量(Tensor)的概念,采用递归张量神经网络(Recursive Neural Tensor Network,RNTN)模型后,通过降低整体的参数数量和计算量,使检测率较RNN模型又提高了7%。实验数据充分说明,采用递归神经网络模型完全可以完成大数据环境下恶意程序的检测分类任务。
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| [1]360互联网安全中心.2018年上半年互联网安全报告[EB/OL].www.anquanke.com/post/id/156689. [2]HINTON G,OSINDERO S,WELLING M,et al.Unsupervised discovery of nonlinear structure using contrastive backpropagation [J].Cognitive Science,2006,30(4):725-731. [3]LV Y,DUAN Y,KANG W,et al.Traffic Flow Prediction With Big Data:A Deep Learning Approach [J].IEEE Transactions on Intelligent Transportation Systems,2015,16(2):865-873. [4]CUI Z,XUE F,CAI X,et al.Detection of Malicious Code Va- riants Based on Deep Learning [J].IEEE Transactions on Industrial Informatics,2018,14(7):3187-3196. [5]DING Y,ZHU S.Malware detection based on deep learning algorithm [J].Neural Computing & Applications,2017(1):1-12. [6]IDIKA N,MATHUR A P.A survey of malware detection techniques[R].Purdue University,2007. [7]PEREVOZCHIKOV V A,SHAYMARDANOV T A,CHU- GUNKOV I V.New techniques of malware detection using FTP Honeypot systems[C]∥Young Researchers in Electrical and Electronic Engineering.IEEE,2017:204-207. [8]YE Y,LI T,ADJEROH D,et al.A survey on malware detection using data mining techniques [J].ACM Computing Surveys (CSUR),2017,50(3):1-40. [9]MAHINDRU A,SINGH P.Dynamic Permissions based An- droid Malware Detection using Machine Learning Techniques[C]∥Innovations in Software Engineering Conference.ACM,2017:202-210. [10]BELLARD F.QEMU,a fast and portable dynamic translator [C]∥Conference on Usenix Technical Conference.USENIX Association,2005:41. [11]HINTON G E.Learning distributed representations of concepts [C]∥Eighth Conference of the Cognitive Science Society.1989. [12]BENGIO Y,VINCENT P,JANVIN C.A neural probabilistic language model [J].Journal of Machine Learning Research,2003,3(6):1137-1155. [13]MNIH A,HINTON G.Three new graphical models for statistical language modelling[C]∥International Conference on Machine Learning.ACM,2007:641-648. [14]MNIH A,HINTON G.A scalable hierarchical distributed language model[C]∥International Conference on Neural Information Processing Systems.Curran Associates Inc.2008:1081-1088. [15]PENNINGTON J,SOCHER R,MANNING C.Glove:Global Vectors for Word Representation[C]∥Conference on Empirical Methods in Natural Language Processing.2014:1532-1543. [16]SOCHER R,MANNING C D,NG A Y.Learning continuous phrase representations and syntactic parsing with recursive neural networks[C]∥Proceedings of the NIPS-2010 Deep Learning and Unsupervised Feature Learning Workshop.2010:1-9. [17]SOCHER R,PERELYGIN A,WU J,et al.Recursive deep models for semantic compositionality over a sentiment treebank[C]∥Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing.2013:1631-1642. |
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