Analysis of KDD Dataset Attributes - Class wise for Intrusion Detection

Author:

Aggarwal Preeti,Sharma Sudhir Kumar

Publisher

Elsevier BV

Subject

General Engineering

Reference21 articles.

1. Jaideep Srivastava, and Aleksandar Lazarevic;Kumar;Managing cyber threats: Issues, approaches, and challenges,2006

2. Maheshkumar Sabhnani and Gursel Serpen, “Why Machine Learning Algorithms Fail in Misuse Detection on KDD Intrusion Detection Data Set”. ACM Transactions on Intelligent Data Analysis, (pp.403-415) (2004).

3. M. Shyu, S. Chen, K. Sarinnapakorn, and L. Chang, “A novel anomaly detection scheme based on principal component classifier”. Proceedings of the IEEE Foundations and New Directions of Data Mining Workshop, in conjunction with the Third IEEE International Conference on Data Mining (ICDM03), pp. 172-179, 2003.

4. KDD Cup 1999. (2014, Nov.) [Online]. Available: http://kdd.ics.uci.edu/databases/kddcup99/.

5. MIT Lincoln Labs. (2014, Nov.). DARPA intrusion detection evaluation [Online]. Available: http://www.ll.mit.edu/mission/communications/ist/corpora/ideval/index.html.

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