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Neural Computing and Applications

, Volume 32, Issue 1, pp 151–161 | Cite as

Development of secured data transmission using machine learning-based discrete-time partially observed Markov model and energy optimization in cognitive radio networks

  • S. VimalEmail author
  • L. Kalaivani
  • M. Kaliappan
  • A. Suresh
  • Xiao-Zhi Gao
  • R. Varatharajan
Brain- Inspired computing and Machine learning for Brain Health

Abstract

The cognitive radio network (CR) is a primary and promising technology to distribute the spectrum assignment to an unlicensed user (secondary users) which is not utilized by the licensed user (primary user).The cognitive radio network frames a reactive security policy to enhance the energy monitoring while using the CR network primary channels. The CR network has a good amount of energy capacity using battery resource and accesses the data communication via the time-slotted channel. The data communication with moderate energy-level utilization during transmission is a great challenge in CR network security monitoring, since intruders may often attack the network in reducing the energy level of the PU or SU. The framework used to secure the communication is using the discrete-time partially observed Markov decision process. This system proposes a modern data communication-secured scheme using private key encryption with the sensing results, and eclat algorithm has been proposed for energy detection and Byzantine attack prediction. The data communication is secured using the AES algorithm at the CR network, and the simulation provides the best effort-efficient energy usage and security.

Keywords

Machine learning Wireless communication Cognitive radio networks Byzantine attack Éclat algorithm 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical responsibilities of the authors

The authors follow the ethical information provided in the journal and hereby abide the same with the journal. I confirm that this work is original and has not been published elsewhere nor it is currently under consideration for publication elsewhere.

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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Department of ITNational Engineering CollegeKovilpattiIndia
  2. 2.Department of EEENational Engineering CollegeKovilpattiIndia
  3. 3.Department of Computer Science and EngineeringNehru Institute of Engineering and TechnologyCoimbatoreIndia
  4. 4.School of ComputingUniversity of Eastern FinlandKuopioFinland
  5. 5.Department of Electronics and Communication EngineeringSri Ramanujar Engineering CollegeKovilpattiIndia

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