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Efficient Intrusion Detection with KNN Classification and DS Theory

  • Deepika Dave
  • Sumit Vashishtha
Conference paper
Part of the Lecture Notes in Bioengineering book series (LNBE)

Abstract

Intrusion detection is an appallingly exigent area of research in the existing scenario. Nowadays, to find a novel pattern of intrusions and detection is an exceedingly difficult job. Our aim is to affect a method for intrusion detection using KNN classification and Dempster theory of evidence. Using these modes, we devised a new pattern of intrusion and classified category of pattern and applied event evidence logic with the help of DS theory. Finned pattern of intrusion is compared with the existing pattern of intrusion which generates a new schema of pattern and updates a list of pattern of intrusion detection and improves the true rate of intrusion detection. We have also accomplished some experimental tasks with KDD99Cup and DARPA98 databases from MIT Lincoln Laboratory which show that the proposed method provides competitively high detection rates compared with other machine learning (ML) techniques and CRISP data mining. The experimental results clearly show that the proposed system achieved higher precision in identifying whether the records are abnormal or attacking ones.

Keywords

Intrusion detection KNN DS theory KDD data set 99 

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Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.Sagar Institute of Research, Technology and ScienceBhopalINDIA

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