Hybrid Data Mining to Reduce False Positive and False Negative Prediction in Intrusion Detection System

  • Bala PalanisamyEmail author
  • Biswajit Panja
  • Priyanka Meharia
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 887)


This paper proposes an approach of data mining machine learning methods for reducing the false positive and false negative predictions in existing Intrusion Detection Systems (IDS). It describes our proposal for building a confidential strong intelligent intrusion detection system which can save data and networks from potential attacks, having recognized movement or infringement regularly reported ahead or gathered midway. We have addressed different data mining methodologies and presented some recommended approaches which can be built together to enhance security of the system. The approach will reduce the overhead of administrators, who can be less concerned about the alerts as they have been already classified and filtered with less false positive and false negative alerts. Here we have made use of KDD-99 IDS dataset for details analysis of the procedures and algorithms which can be implemented.


Intrusion Detection Systems Data mining Intrusion detection Anomaly detection SVM KNN ANN 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Bala Palanisamy
    • 1
    Email author
  • Biswajit Panja
    • 1
  • Priyanka Meharia
    • 1
  1. 1.Department of Computer ScienceEastern Michigan UniversityYpsilantiUSA

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