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GRU-SVM Based Threat Detection in Cognitive Radio Network.

Evelyn Ezhilarasi I1, J Christopher Clement1

  • 1School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India.

Sensors (Basel, Switzerland)
|February 11, 2023
PubMed
Summary

This study introduces a novel approach using gated recurrent units (GRU) and support vector machine (SVM) to detect malicious users in cognitive radio networks (CRN). The combined model significantly enhances spectrum sensing security and network performance.

Keywords:
cognitive radio networkgated recurrent unitmalicious usersspectrum sensingsupport vector machine

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Telecommunications

Background:

  • Cognitive radio networks (CRN) face significant security challenges during spectrum sensing, with malicious users degrading network performance.
  • Machine learning and deep learning offer promising solutions for detecting network anomalies and threats in CRN.
  • Gated recurrent units (GRU), a type of deep learning model, are underutilized in CRN despite their effectiveness with smaller datasets.

Purpose of the Study:

  • To investigate the application of gated recurrent units (GRU) for spectrum sensing in cognitive radio networks.
  • To combine GRU with a support vector machine (SVM) classifier to enhance the detection of malicious users.
  • To evaluate the performance of the proposed GRU-SVM model in identifying authorized and unauthorized users.

Main Methods:

  • Utilized gated recurrent units (GRU), a lightweight variant of LSTM, for training and testing spectrum sensing data.
  • Employed a support vector machine (SVM) classifier in the output layer for distinguishing between authorized and malicious users.
  • Developed a novel combined model integrating GRU and SVM specifically for cognitive radio network applications.

Main Results:

  • Achieved a high testing accuracy of 82.45% and a training accuracy of 80.99%.
  • Demonstrated a perfect detection probability of 1 for malicious users.
  • The proposed GRU-SVM model showed effectiveness at 65 epochs.

Conclusions:

  • The integration of GRU and SVM presents a novel and effective solution for enhancing security in cognitive radio networks.
  • GRU models offer a computationally efficient approach for deep learning applications in CRN, particularly with limited data.
  • The proposed method significantly improves the detection of malicious users, thereby bolstering the overall performance and reliability of CRN.