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A novel density estimation based intrusion detection technique with Pearson's divergence for Wireless Sensor

Shashank Gavel1, Ajay Singh Raghuvanshi1, Sudarshan Tiwari2

  • 1Department of Electronics and Telecommunication, National Institute of Technology Raipur, India.

ISA Transactions
|December 5, 2020
PubMed
Summary

This study introduces a novel network intrusion detection technique using multi-varying kernel density estimation and distributed computing to identify long-lasting attacks from compromised nodes. The method achieves efficient detection with low false positive rates, enhancing system stability.

Keywords:
Anomaly and intrusion detectionDistributed and centralized computingKernel based density estimationPearson’s divergenceWSNWireless Sensor Networks

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Area of Science:

  • Computer Science
  • Network Security
  • Data Analysis

Background:

  • Intrusive network attacks from compromised nodes can persist, degrading sensor readings and potentially causing system failure.
  • Existing methods may struggle with the prolonged nature and impact of these sophisticated attacks.

Purpose of the Study:

  • To develop and evaluate a novel technique for detecting persistent intrusive network attacks.
  • To improve the efficiency and accuracy of intrusion detection systems, particularly in the presence of compromised nodes.

Main Methods:

  • A combination of multi-varying kernel density estimation and distributed computing to analyze data probabilities and calculate global Probability Density Functions (PDFs).
  • Application of Pearson's divergence (PE) for efficient in-network intrusion detection and estimation, with PDF approximation via distributed computing techniques.
  • Development of an entropy-based method using a centralized computing approach for comparison.

Main Results:

  • The proposed technique effectively detects intrusive attacks with a low False Positive Rate (FPR).
  • Pearson's divergence demonstrated robust performance in intrusion estimation.
  • Comparison with an entropy-based method validated the robustness of the PE divergence approach.

Conclusions:

  • The novel technique combining multi-varying kernel density estimation and distributed computing offers an effective solution for detecting persistent network intrusions.
  • The use of Pearson's divergence provides efficient and accurate intrusion detection with low FPRs.
  • The proposed algorithms show promising results on real-world datasets, outperforming or matching existing methods in key metrics.