Kenkre P S, Pai A, Colaco L. Real time intrusion detection and prevention system[C]//Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014. Cham: Springer, 2015: 405-411
[2] Anderson J P. Computer security threat monitoring and surveillance[R].Co Fort Washington PA:James P. Anderson Company, 1980
[3] Bai Yuebin, Kobayashi H. Intrusion detection systems: technology and development[C]//Advanced Information Networking and Applications, 2003. AINA 2003.17th International Conference on. IEEE, 2003: 710-715
[4] Sabhnani M, Serpen G. Application of Machine Learning Algorithms to KDD Intrusion Detection Dataset within Misuse Detection Context[C]// International Conference on Machine Learning; Models, Technologies and Applications. Las Vegas, Nevada: DBLP, 2003:209-215
[5] Mahmood H A. Network Intrusion Detection System (NIDS) in Cloud Environment based on Hidden Naïve Bayes Multiclass Classifier[J]. Al-Mustansiriyah Journal of Science, 2018, 28(2): 134-142
[6] Nguyen S N, Nguyen V Q, Choi J, et al. Design and implementation of intrusion detection system using convolutional neural network for DoS detection[C]//Proceedings of the 2nd International Conference on Machine Learning and Soft Computing. New York: ACM, 2018: 34-38
[7] Mukkamala S, Janoski G, Sung A. Intrusion detection: support vector machines and neural networks[C]//proceedings of the IEEE International Joint Conference on Neural Networks (ANNIE), St. Louis, MO. 2002: 1702-1707
[8] Osuna E, Freund R, Girosit F. Training support vector machines: an application to face detection[C]//Computer vision and pattern recognition, 1997. Proceedings., 1997 IEEE computer society conference on. IEEE, 1997: 130-136
[9] Zhang Chunlin, Jiang Ju, Kamel M. Intrusion detection using hierarchical neural networks[J]. Pattern Recognition Letters, 2005, 26(6): 779-791
[10] Liu Yuchen, Liu Shengli, Zhao Xing. Intrusion Detection Algorithm Based on Convolutional Neural Network[J]. Beijing LigongDaxueXuebao/transaction of Beijing Institute of Technology, 2017, 37(12):1271-1275
[11] Aslahi-Shahri B M, Rahmani R, Chizari M, et al. A hybrid method consisting of GA and SVM for intrusion detection system[J]. Neural computing and applications, 2016, 27(6): 1669-1676
[12] KeGuolin, Meng Qin, Finley T, et al. Lightgbm: A highly efficient gradient boosting decision tree[C]//Advances in Neural Information Processing Systems.Long Beach, CA: NIPS, 2017: 3146-3154
[13] Jensen T R, Toft B. Graph coloring problems[M]. Hoboken, NJ:John Wiley & Sons, 2011
[14] Lee W, Stolfo S J. A framework for constructing features and models for intrusion detection systems[J]. Acm Transactions on Information & System Security, 2000, 3(4):227-261
[15] Wang Ming, Li Jian. Network Intrusion Detection Model Based on Convolutional Neural Network[J]. Journal of Information Security Research, 2017, 3(11):990-994
[16] Moustafa N, Slay J. The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set[J]. Information Security Journal: A Global Perspective, 2016, 25(1-3): 18-31
[17] Polikar R. Ensemble learning[M]//Ensemble machine learning. Boston, MA: Springer, 2012: 1-34
[18] Lee K B, Goo H W. Quantitative Image Quality and Histogram-Based Evaluations of an Iterative Reconstruction Algorithm at Low-to-Ultralow Radiation Dose Levels: A Phantom Study in Chest CT[J]. Korean Journal of Radiology, 2018, 19(1): 119-129
[19] Cheong S, Oh S H, Lee S Y. Support vector machines with binary tree architecture for multi-class classification[J]. Neural Information Processing-Letters and Reviews, 2004, 2(3): 47-51
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