Detection of Outliers Using Interquartile Range Technique from Intrusion Dataset

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 701)

Abstract

Unpredictable usage of Internet adds more problems to the network. Protecting the system from the anomalous behavior plays a major issue in NIDS. Data mining approaches in the field of Intrusion Detection System (IDS) is becoming more popular. The outlier is a current problem faced by many data mining researches. Outliers are the patterns which are not in the range of normal behavior. Outliers in the dataset produce more false positive alarms, and this has to be reduced to increase the efficiency of IDS. We have used Interquartile Range technique to identify the outliers in the NSLKDD’99. In this, the continuous range of input is divided into quartiles and these quartiles are analyzed to target the range of outliers. Then the obtained outliers are removed by a filter called remove with value. The experiment is conducted using Weka data mining tool.

Keywords

NIDS Outlier Interquartile range Remove with value 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.CS&E DepartmentBapuji Institute of Education & TechnologyDavangereIndia
  2. 2.IS&E DepartmentBapuji Institute of Education & TechnologyDavangereIndia
  3. 3.IS&E DepartmentRVCEBengaluruIndia

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