A hybrid machine learning approach to network anomaly detection
暂无分享,去创建一个
[1] Dong Xiang,et al. Information-theoretic measures for anomaly detection , 2001, Proceedings 2001 IEEE Symposium on Security and Privacy. S&P 2001.
[2] Amit Konar,et al. Supervised learning on a fuzzy Petri net , 2005, Inf. Sci..
[3] Craig H. Rowland,et al. Covert Channels in the TCP/IP Protocol Suite , 1997, First Monday.
[4] Rick Wilder,et al. Wide-Area Internet Traffic Patterns and Characteristics ( Extended Version ) , 1997 .
[5] Thorsten Joachims,et al. Making large scale SVM learning practical , 1998 .
[6] Thorsten Joachims,et al. Estimating the Generalization Performance of an SVM Efficiently , 2000, ICML.
[7] Teuvo Kohonen,et al. The self-organizing map , 1990, Neurocomputing.
[8] B. Ravichandran,et al. Statistical traffic modeling for network intrusion detection , 2000, Proceedings 8th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (Cat. No.PR00728).
[9] Kimberly Claffy,et al. Internet traffic characterization , 1994 .
[10] Sung-Bae Cho,et al. Ensemble of structure-adaptive self-organizing maps for high performance classification , 2000, Inf. Sci..
[11] R. Wilder,et al. Wide-area Internet traffic patterns and characteristics , 1997, IEEE Netw..
[12] John H. Holland,et al. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .
[13] Susan T. Dumais,et al. Hierarchical classification of Web content , 2000, SIGIR '00.
[14] V. Rao Vemuri,et al. Robust Support Vector Machines for Anomaly Detection in Computer Security , 2003, ICMLA.
[16] Dorothea Heiss-Czedik,et al. An Introduction to Genetic Algorithms. , 1997, Artificial Life.
[17] G. G. Stokes. "J." , 1890, The New Yale Book of Quotations.
[18] Salvatore J. Stolfo,et al. AI Approaches to Fraud Detection and Risk Management , 1998, AI Mag..
[19] David C. Yen,et al. Classification methods in the detection of new malicious emails , 2005, Inf. Sci..
[20] Deepa Kundur,et al. Practical Data Hiding in TCP/IP , 2002 .
[21] Markus G. Kuhn,et al. Analysis of a denial of service attack on TCP , 1997, Proceedings. 1997 IEEE Symposium on Security and Privacy (Cat. No.97CB36097).
[22] Andrew T. Phillips,et al. Comparing the template method and strategy design patterns in a genetic algorithm application , 2002, SGCS.
[23] Andrew W. Moore,et al. Locally Weighted Learning for Control , 1997, Artificial Intelligence Review.
[24] Andrew W. Moore,et al. Locally Weighted Learning , 1997, Artificial Intelligence Review.
[25] B. C. Brookes,et al. Information Sciences , 2020, Cognitive Skills You Need for the 21st Century.
[26] Massimiliano Pontil,et al. Properties of Support Vector Machines , 1998, Neural Computation.
[27] Giovanni Vigna,et al. Testing network-based intrusion detection signatures using mutant exploits , 2004, CCS '04.
[28] Bernhard Schölkopf,et al. Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.
[29] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[30] Young-Seuk Park,et al. Self-Organizing Map , 2008 .
[31] Andrew H. Sung,et al. Feature Selection for Intrusion Detection with Neural Networks and Support Vector Machines , 2003 .
[32] A.H. Sung,et al. Identifying important features for intrusion detection using support vector machines and neural networks , 2003, 2003 Symposium on Applications and the Internet, 2003. Proceedings..
[33] Sridhar Ramaswamy,et al. Efficient algorithms for mining outliers from large data sets , 2000, SIGMOD '00.
[34] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[35] Hyeran Byun,et al. A Survey on Pattern Recognition Applications of Support Vector Machines , 2003, Int. J. Pattern Recognit. Artif. Intell..
[36] Stuart Staniford-Chen,et al. Practical Automated Detection of Stealthy Portscans , 2002, J. Comput. Secur..
[37] Leonid Portnoy,et al. Intrusion detection with unlabeled data using clustering , 2000 .
[38] Risto Miikkulainen,et al. Intrusion Detection with Neural Networks , 1997, NIPS.
[39] H. Javitz,et al. Detecting Unusual Program Behavior Using the Statistical Component of the Next-generation Intrusion Detection Expert System ( NIDES ) 1 , 1997 .
[40] Eleazar Eskin,et al. A GEOMETRIC FRAMEWORK FOR UNSUPERVISED ANOMALY DETECTION: DETECTING INTRUSIONS IN UNLABELED DATA , 2002 .
[41] Salvatore J. Stolfo,et al. One Class Support Vector Machines for Detecting Anomalous Windows Registry Accesses , 2003 .
[42] Marti A. Hearst. Trends & Controversies: Support Vector Machines , 1998, IEEE Intell. Syst..