Computer Science ›› 2021, Vol. 48 ›› Issue (3): 327-332.doi: 10.11896/jsjkx.200600025
• Information Security • Previous Articles
LIU Quan-ming, LI Yin-nan, GUO Ting, LI Yan-wei
CLC Number:
[1]KIM J,KIM J,THU H L,et al.Long Short Term Memory Recurrent Neural Network Classifier for Intrusion Detection[C]//International Conference on Platform Technology and Service.2016:1-5. [2]SHON T,MOON J.A hybrid machine learning approach to network anomaly detection[J].Information Sciences,2007,177(18):3799-3821. [3]TAN B,TAN Y,LI Y X,et al.Research on Intrusion Detection System Based on Improved Pso-svm Algorithm[J].Chemical Engineering Transactions,2016:583-588. [4]ZHAO Y H.Research on intrusion detection Optimization Algorithm based on SVM active learning[J].Journal of Jingchu University of Technology,2018,33(4):5-9. [5]REN J D,LIU X Q,WANG Q,et al.An Multi-Level Intrusion Detection Method Based on KNN Outlier Detection and Random Forests[J].Journal of Computer Research and Development,2019,56(3):566-575. [6]SCHMIDHUBER J.Deep learning in neural networks:An overview[J].Neural Networks,2015,61:85-117. [7]RAFF E,SYLVESTER J,NICHOLAS C,et al.Learning the PE Header,Malware Detection with Minimal Domain Knowledge[J].Machine Learning,2017:121-132. [8]SHI L Y,ZHU H Q,LIU Y H,et al.Intrusion Detection of Industrial Control System Based on Correlation Information Entropy and CNN-BiLSTM[J].Journal of Computer Research and Development,2019,56(11):2330-2338. [9]WANG M,LI J.Network Intrusion Detection Model Based on Convolutional Neural Network[J].Journal of Information Security Research,2017,3(11):990-994. [10]PHETLASY S,OHZAHATA S,WU C,et al.ApplyingSMOTE for a Sequential Classifiers Combination Method to Improve the Performance of Intrusion Detection System[C]//Dependable Autonomic and Secure Computing.2019:255-258. [11]DING H W,WAN L,LONG T Y.Research on the application of deep auto-encoder network in intrusion detection[J].Journal of Harbin Institute of Technology,2019,51(5):185-194. [12]HUI H,WANG W Y,MAO B H.Borderline-SMOTE:a newover-sampling method in imbalanced data sets learning[C]//International Conference on Intelligent Computing.Berlin,Heidelberg:Springer,2005. [13]MNIH V,HEESS N ,GRAVES A ,et al.Recurrent Models of Visual Attention[J].arXiv:1406.6247v1,2014. [14]WOO S,PARK J,LEE J,et al.CBAM:Convolutional Block Attention Module[C]//European Conference on Computer Vision.2018:3-19. [15]PHETLASY S,OHZAHATA S,WU C,et al.ApplyingSMOTE for a Sequential Classifiers Combination Method to Improve the Performance of Intrusion Detection System[C]//Dependable Autonomic and Secure Computing.2019:255-258. [16]LI Y,ZHANG B.An Intrusion DetectionAlgorithm Based onDeep CNN[J].Computer Applications and Software,2020,37(4):324-328. [17]DING H W,WAN L,ZHOU K,et al.Study on Intrusion Detection Based on Deep Convolution Neural Network[J].Computer Science,2019,46(10):173-179. [18]LIAN H F,ZHANG H,GUO W Z.Netflow Anomaly Detection Based on Data Enhancement and Hybrid Neural Network [J].Journal of Chinese Mini-Micro Computer Systems,2020,41(4):786-793. [19]YANG Y,ZHENG K,WU C,et al.Building an Effective Intrusion Detection System Using the Modified Density Peak Clustering Algorithm and Deep Belief Networks[J].Applied Sciences,2019,9(2):238. [20]THASEEN I S,KUMAR C A.Intrusion detection model using fusion of chi-square feature selection and multi class SVM[J].Journal of King Saud University-Computer and Information Sciences,2017,29(4):462-472. [21]PARSAEI M R,ROSTAMI S M,JAVIDAN R,et al.A Hybrid Data Mining Approach for Intrusion Detection on Imbalanced NSL-KDD Dataset[J].International Journal of Advanced Computer Science and Applications,2016,7(6):20-25. [22]YANG Y R,SONG R J,ZHOU Z Y.Network Intrusion Detection Method Based on GAN-PSO-ELM[J].Computer Enginee-ring and Applications,2020,56(12):66-72. |
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