[1] XU Jian,XUE Yongjun.Research on the application of machine learning theory in intrusion detection techno-logy[J].Informatization Research,2014,40(3):6-8,12.(in Chinese)徐建,薛永隽.机器学习理论在入侵检测技术中的应用研究[J].信息化研究,2014,40(3):6-8,12. [2] SONG Huazhi,MA Yutao.DeepTriage:an automatic triage method for software bugs using deep learning[J].Journal of Chinese Computer Systems,2019,40(1):126-132.(in Chinese)宋化志,马于涛.DeepTriage:一种基于深度学习的软件缺陷自动分配方法[J].小型微型计算机系统,2019,40(1):126-132. [3] INGRE B,YADAV A.Performance analysis of NSL-KDD dataset using ANN[C]//Proceedings of 2015 International Conference on Signal Processing and Communication Engineering Systems.Washington D.C.,USA:IEEE Press,2015:92-96. [4] WANG Yang,WU Zhongdong,HUO Zhongcai.Intrusion detection algorithm based on DBN-KELM[J].Computer Engineering,2019,45(10):171-175,182.(in Chinese)汪洋,伍忠东,火忠彩.基于DBN-KELM的入侵检测算法[J].计算机工程,2019,45(10):171-175,182. [5] MIRZA A H,COSAN S.Computer network intrusion detection using sequential LSTM neural networks autoencoders[C]//Proceedings of 2018 Signal Processing and Communications Applications Conference.Washington D.C.,USA:IEEE Press,2018:1-4. [6] HU Wenjun,FU Meijun,PAN Wenlin.Primi speech recognition based on Kaldi[J].Computer Engineering,2018,44(1):199-205.(in Chinese)胡文君,傅美君,潘文林.基于Kaldi的普米语语音识别[J].计算机工程,2018,44(1):199-205. [7] HASTIE T,TIBSHIRANI R,FRIEDMAN J.Ensemble Learning[M].Berlin,Germany:Springer,2009. [8] PODGORELEC V,ZORMAN M.Decision tree learning[M].Berlin,Germany:Springer,2015. [9] GUO Hui,LIU Zhongbao,LIU Xin.Intrusion detection method based on cloud model and decision tree[J].Computer Engineering,2019,45(4):142-147.(in Chinese)郭慧,刘忠宝,柳欣.基于云模型与决策树的入侵检测方法[J].计算机工程,2019,45(4):142-147. [10] ZHOU Zhihua.Machine Learning[M].Beijing:Tsinghua University Press,2016.(in Chinese)周志华.机器学习[M].北京:清华大学出版社,2016. [11] BREIMAN L.Random forests[J].Machine Learning,2001,45(1):5-32. [12] LIAW A,WIENER M.Classification and regression by random forest[J].R News,2002(2/3):18-22. [13] ZHOU Zhihua,FENG Ji.Deep forest:towards an alternative to deep neural networks[EB/OL].[2018-10-03].https://arxiv.org/pdf/1702.08835v1.pdf. [14] XU Yin,WANG Ruilin,LIU Xiaobo,et al.Deep forest-based classification of hyperspectral images[C]//Proceedings of the 37th Chinese Control Conference.Wuhan,China:Chinese Association of Automation,2018:10367-10373. [15] TAVALLAEE M,BAGHERI E,LU W,et al.A detailed analysis of the KDD CUP 99 data set[C]//Proceedings of the 2nd IEEE Symposium on Computational Intelligence for Security and Defense Applications.Washington D.C.,USA:IEEE Press,2009:1-6. [16] RASCHKA S.Python machine learning[M].[S.l.]:Packt Publishing,2015. [17] REVATHI S,MALATHI A.A detailed analysis on NSL-KDD dataset using various machine learning techniques for intrusion detection[J].International Journal of Engineering Researchand Technology,2013,2(12):1848-1854. [18] JIAN Zhigang,JIN Xu.Research on data preprocess in data mining and its application[J].Application Research of Computers,2004,21(7):117-119.(in Chinese)菅志刚,金旭.数据挖掘中数据预处理的研究与实现[J].计算机应用研究,2004,21(7):117-119. [19] QIAN Tieyun,WANG Yi,ZHANG Mingming,et al.Intrusion detection method based on deep neural network[J].Journal of Huazhong University of Science and Technology(Nature Science Edition),2018,46(1):6-10.(in Chinese)钱铁云,王毅,张明明,等.基于深度神经网络的入侵检测方法[J].华中科技大学学报(自然科学版),2018,46(1):6-10. [20] YAO Li,ZHANG Xihuang.An improved random forest algorithm for multi-class imbalanced data problem under MapReduce[J].Microelectronics and Computer,2018,35(11):139-144.(in Chinese)姚立,张曦煌.MapReduce环境下处理多类别不平衡数据的改进随机森林算法[J].微电子学与计算机,2018,35(11):139-144. |