The precocious classification framework for network intrusion detection system


  • D. Selvamani Department of Computer Science, Mother Teresa Women’s University, Kodaikanal, Tamil Nadu
  • V. Selvi Department of Computer Science, Mother Teresa Women’s University, Kodaikanal, Tamil Nadu





Intrusion Detection System, KDD CUP 99, Feature Selection, Information Gain, Artificial Neural Network


The Intrusion Detection System (IDS) can be used broadly for securing the network. Intrusion detection systems (IDS) are typically positioned laterally through former protecting safety automation, like access control and verification, as a subsequent line of resistance that guards data classifications. This projected framework established on the precocious feature selection, which is consent to lessens the number of features generated in the KDD CUP 99 benchmark dataset. The projected framework customs the Back Propagation Neural Networks to recognize the Denial of Service (DoS), where it is a combined variety of attack in the networks.




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