Incremental SVM based on reserved set for network intrusion detection
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Wei Xu | Yang Yi | Jiansheng Wu | W. Xu | Jiansheng Wu | Yang-Ming Yi
[1] Tai-Myoung Chung,et al. Effective Value of Decision Tree with KDD 99 Intrusion Detection Datasets for Intrusion Detection System , 2008, 2008 10th International Conference on Advanced Communication Technology.
[2] Gu Hongying. DoS Intrusion Detection Based on Incremental Learning with Support Vector Machines , 2006 .
[3] Yongdae Kim,et al. A machine learning framework for network anomaly detection using SVM and GA , 2005, Proceedings from the Sixth Annual IEEE SMC Information Assurance Workshop.
[4] Cheng Xiang,et al. Design of Multiple-Level Hybrid Classifier for Intrusion Detection System , 2005, 2005 IEEE Workshop on Machine Learning for Signal Processing.
[5] Simin Nadjm-Tehrani,et al. Adaptive real-time anomaly detection with incremental clustering , 2007, Inf. Secur. Tech. Rep..
[6] Fei Ren,et al. Using Density-Based Incremental Clustering for Anomaly Detection , 2008, 2008 International Conference on Computer Science and Software Engineering.
[7] Eleazar Eskin,et al. Anomaly Detection over Noisy Data using Learned Probability Distributions , 2000, ICML.
[8] Surat Srinoy,et al. Intrusion Detection Model Based On Particle Swarm Optimization and Support Vector Machine , 2007, 2007 IEEE Symposium on Computational Intelligence in Security and Defense Applications.
[9] V. Rao Vemuri,et al. Robust Support Vector Machines for Anomaly Detection in Computer Security , 2003, ICMLA.
[10] Huan Liu,et al. Handling concept drifts in incremental learning with support vector machines , 1999, KDD '99.
[11] Jung-Min Park,et al. Network anomaly detection with incomplete audit data , 2007, Comput. Networks.
[12] Zhang Hong-da. New algorithm for SVM-Based incremental learning , 2006 .
[13] Hong Shen,et al. Application of online-training SVMs for real-time intrusion detection with different considerations , 2005, Comput. Commun..
[14] Ajith Abraham,et al. Modeling intrusion detection system using hybrid intelligent systems , 2007, J. Netw. Comput. Appl..