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Study on Classification Algorithms for Network Intrusion Systems 被引量:5

Study on Classification Algorithms for Network Intrusion Systems
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出处 《通讯和计算机(中英文版)》 2012年第11期1242-1246,共5页 Journal of Communication and Computer
关键词 网络攻击 分类算法 入侵系统 入侵检测系统 决策树算法 信息安全 混淆矩阵 IDS Intrusion detection, decision trees, J48, random forest, random tree, NSL-KDD, WEKA.
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参考文献13

  • 1C. Kruegel, F. Valeur, G. Vigna, Intrusion Detection and Correlation Challenges and Solutions, Springer Science, Boston, 2005.
  • 2J. Zhang, M. Zulkernine, Random-forests-based network intrusion detection systems, IEEE Transactions on Systems Man and Cybernetics 38 1,2008) 649-659.
  • 3H.A. Nguyen, D. Choi, Application of Data Mining to Network Intrusion Detection: Classifier Selection Model, In Challenges for Next Generation Network Operations and Service Management, Springer Berlin, Heidelberg, 2008.
  • 4D.M. Farid, N. Harbi, M.Z bayes and decision tree for International Journal of Rahman, Combining naive adaptive intrusion detection, Network Security & ItsApplications (IJNSA) 2 (2010) 12-25.
  • 5NSL-KDD Dataset for Network-based Intrusion Detection Systems, http://nsl.cs.unb.ca/NSL-KDD/(accessed March 2009).
  • 6S. Lakhina, S. Joseph, B. Verma, Feature reduction using principal component analysis for effective anomaly-based intrusion detection on NSL-KDD, International Journal of Engineering Science and Technology 2 (2010) 1790-1799.
  • 7M. Tavallaee, E. Bagheri, W. Lu, A.A. Ghorbani, A detailed analysis of the KDD CUP 99 dataset, in: Proceedings of the 2009 IEEE Symposium on Computational Intelligence in Security and Defense Applications, Canada, 2009.
  • 8M.W. Berry, M. Browne, Lecture Notes in Data Mining, World Scientific Publishing Co. Pte. Ltd, 2006.
  • 9J.W. Han, M, Kamber, Data Mining Concepts and Techniques, Morgan Kaufmann Publishers, Elsevier Inc, 2006.
  • 10N. Gayatri, S. Nickolas, A.V. Reddy, Feature selection using decision tree induction in class level metrics dataset for software defect predictions, in: Proceedings of the World Congress on Engineering and Computer Science 2010, San Francisco, USA, 2010.

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