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A Hypergraph and Arithmetic Residue-based Probabilistic Neural Network for classification in Intrusion Detection

M R Gauthama Raman1, Nivethitha Somu1, Kannan Kirthivasan2

  • 1Centre for Information Super Highway (CISH), School of Computing, SASTRA University, Thanjavur- 613401, Tamil Nadu, India.

Neural Networks : the Official Journal of the International Neural Network Society
|March 27, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Intrusion Detection System (IDS) using Helly property of Hypergraph and Arithmetic Residue-based Probabilistic Neural Network (HG AR-PNN). The new method enhances detection rates for rare attacks and improves IDS stability.

Keywords:
Arithmetic Residue (AR)Artificial Neural Network (ANN)Feature selection and classificationHypergraph (HG)Intrusion Detection Systems (IDSs)Probabilistic Neural Network (PNN)

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Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Machine Learning

Background:

  • Intrusion Detection Systems (IDS) are crucial for network security.
  • Artificial Neural Network (ANN) models improve IDS performance but face stability and detection rate trade-offs for infrequent attacks.

Purpose of the Study:

  • To present a novel approach, Helly property of Hypergraph and Arithmetic Residue-based Probabilistic Neural Network (HG AR-PNN), for IDS classification.
  • To address the challenge of detecting less frequent attacks while maintaining IDS stability.

Main Methods:

  • Exploiting the Helly property of Hypergraph for optimal feature subset identification.
  • Utilizing the arithmetic residue of the optimal feature subset to train a Probabilistic Neural Network (PNN).
  • Evaluating the HG AR-PNN classifier performance on the KDD CUP 1999 intrusion dataset.

Main Results:

  • The HG AR-PNN classifier demonstrated superior performance compared to existing classifiers.
  • Achieved improved detection rates for less frequent attacks.
  • Showcased enhanced stability in the Intrusion Detection System architecture.

Conclusions:

  • The proposed HG AR-PNN approach effectively overcomes the limitations of traditional ANN-based IDSs.
  • This novel method offers a promising solution for robust and stable network intrusion detection.
  • The findings highlight the potential of hypergraph properties and arithmetic residue in advanced cybersecurity.