Computer Science ›› 2021, Vol. 48 ›› Issue (11A): 705-712.doi: 10.11896/jsjkx.201100101
• Interdiscipline & Application • Previous Articles Next Articles
HUAN Wen-ming, LIN Hai-tao
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[1]GAMAGE S,SAMARABANDU J.Deep learning methods innetwork intrusion detection:A survey and an objective comparison[J].Journal of Network and Computer Applications,2020,169:102767. [2]AMBUSAIDI M A,HE X,NANDA P,et al.Building an intrusion detection system using a filter-based feature selection algorithm[J].IEEE Transactions on Computers,2016,65(10):2986-2998. [3]AL-QATF M,LASHENG Y,AL-HABIB M,et al.Deep learning approach combining sparse autoencoder with SVM for network intrusion detection[J].IEEE Access,2018,6:52843-52856. [4]ZHOU Y,CHENG G,JIANG S,et al.Building an efficient intrusion detection system based on feature selection and ensemble classifier[J].Computer Networks,2020,174:297-304. [5]YIJING L,HAIXIANG G,XIAO L,et al.Adapted ensembleclassification algorithm based on multiple classifier system and feature selection for classifying multi-class imbalanced data[J].Knowledge-Based Systems,2016,94:88-104. [6]FERNÁNDEZ A,GARCÍA S,GALAR M,et al.ImbalancedClassification with Multiple Classes [M].Learning from Imbalanced Data Sets.Cham,Springer International Publishing.2018:197-226. [7]LI Y X,CHAI Y,HU Y Q,et al.Review of imbalanced dataclassification methods[J].Control and Decision,2019,34(4):673-688. [8]LIN W C,TSAI C F,HU Y H,et al.Clustering-based undersampling in class-imbalanced data[J].Information Sciences,2017,409/410:17-26. [9]CHAWLA N V,BOWYER K W,HALL L O,et al.SMOTE:Synthetic Minority Over-sampling Technique[J].Journal of Artificial Intelligence Research,2002,16(1):321-357. [10]ZHANG X Y,WANG H Z.Intrusion Detection of ICS Based on Improved BorderSMOTE for Unbalance Data[J].Netinfo Security,2020,20(7):70-76. [11]ZHANG H,HUANG L,WU C Q,et al.An effective convolutional neural network based on SMOTE and Gaussian mixture model for intrusion detection in imbalanced dataset[J].Compu-ter Networks,2020,177:303-315. [12]WU Y X,WANG J L,YANG L,et al.Survey on cost-sensitive Deep Learning Methods[J].Computer Science,2019,46(5):8-19. [13]TELIKANI A,GANDOMI A H.Cost-sensitive stacked auto-encoders for intrusion detection in the Internet of Things[J].Internet of Things,2019,14:157-169. [14]HAIXIANG G,YIJING L,SHANG J,et al.Learning fromclass-imbalanced data:Review of methods and applications[J].Expert Systems with Applications,2016,73(MAY):220-239. [15]SHAHRAKI A,ABBASI M,HAUGEN Ø.Boosting algorithms for network intrusion detection:A comparative evaluation of Real AdaBoost,Gentle AdaBoost and Modest AdaBoost[J].Engineering Applications of Artificial Intelligence,2020,94:103770. [16]GALAR M,FERNANDEZ A,BARRENECHEA E,et al.A Review on Ensembles for the Class Imbalance Problem:Bagging-,Boosting-,and Hybrid-Based Approaches[J].IEEE Transactions on Systems,Man,and Cybernetics,Part C (Applications and Reviews),2012,42(4):463-484. [17]SEIFFERT C,KHOSHGOFTAAR T M,VAN HULSE J,et al.RUSBoost:A hybrid approach to alleviating class imbalance[J].IEEE Transactions on Systems,Man,and Cybernetics Part A:Systems and Humans,2010,40(1):185-197. [18]CHAWLA N V,LAZAREVIC A,HALL L O,et al.SMOTEBoost:Improving prediction of the minority class in Boosting[C]//The 7th European Conference on Principles and Practice of Knowledge Discovery in Databases.Springer Verlag,2003:107-119. [19]DIEZ-PASTOR J F,RODRIGUEZ J J,GARCIA-OSORIO C,et al.Random Balance:Ensembles of variable priors classifiers for imbalanced data[J].Knowledge-Based Systems,2015,85:96-111. [20]FREUND Y,SCHAPIRE R E.A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting[J].Journal of Computer and System Sciences,1997,55(1):119-139. [21]KDD Cup 1999 Data[EB/OL].http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html. [22]TAVALLAEE M,BAGHERI E,LU W,et al.A detailed analysis of the KDD CUP 99 data set[C]//IEEE Symposium on Computational Intelligence for Security and Defense Applications.2009:1-6. [23]HONG J H,MIN J K,CHO U K,et al.Fingerprint classification using one-vs-all support vector machines dynamically ordered with naïve Bayes classifiers[J].Pattern Recognition,2008,41(2):662-671. [24]GAO X,SHAN C,HU C,et al.An Adaptive Ensemble Machine Learning Model for Intrusion Detection[J].IEEE Access,2019,7:82512-82521. [25]KASONGO S M,SUN Y.A Deep Learning Method with FilterBased Feature Engineering for Wireless Intrusion Detection system[J].IEEE Access,2019:38597-38607. |
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