Performance Comparison between Backpropagation Algorithms Applied to Intrusion Detection in Computer Network Systems

نویسندگان

  • Iftikhar Ahmad
  • M. A Ansari
  • Sajjad Mohsin
چکیده

In this paper a topology of neural network intrusion detection system is proposed on which different backpropagation algorithms are benchmarked. The proposed methodology uses sampled data from KddCup99 data set, an intrusion detection attacks database that is a standard for the evaluation of intrusion detection systems. The performance of backpropagation algorithms implemented in batch mode, is addressed. A comparative analysis of algorithms is made and then the most optimum solution is selected with respect to mean square error. Key-Words: Intrusion Detection, Backpropagation, Neural Networks, Intrusion Detection System, Learning Algorithm, Dataset.

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تاریخ انتشار 2008