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Enhancement of Security in GFDM Using Ebola-Optimized Joint Secure Compressive Sensing Encryption and Symbol Scrambling Model

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The 3rd International Conference on Artificial Intelligence and Computer Vision (AICV2023), March 5–7, 2023 (AICV 2023)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 164))

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Abstract

The security of data transmission over the Passive Optical Network (PON) is a very challenging task. In this research work, joint secure compressive sensing encryption and symbol scrambling method is introduced in the Generalized Frequency Division Multiplexing (GFDM) systems to overcome this problem. After compression, CS data is converted into Binary Phase Shift Keying (BPSK) symbols, the SHA-256 technique is used for key generation purposes, and the key selection process is performed to improve the high security of the proposed system. Ebola meta-heuristic scheme is proposed for the optimal key selection process. The maximum Peak-To-Signal Noise Ratio (PSNR) value is the Objective Function (OF) of this proposed key selection to choose the optimal secret key from the random numbers. Finally, the symbol scrambling function is performed on the resulting BPSK symbols to improve the security of the CS data. The performance of this proposed system is compared with some of the existing methods The achieved values of the proposed GFDM-PON for PSNR, MSE, Entropy, encryption time, and decryption time are 57.86319, 0.004114, 9.495201, 0.054, and 0.048, respectively.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Lovely Professional University, Punjab, India

    Irfan Ahmad Rather, Gulshan Kumar & Rahul Saha

  2. Konkuk University, Seoul, South Korea

    Tai-hoon Kim

Authors
  1. Irfan Ahmad Rather
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  2. Gulshan Kumar
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  3. Rahul Saha
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  4. Tai-hoon Kim
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Corresponding author

Correspondence to Gulshan Kumar .

Editor information

Editors and Affiliations

  1. Faculty of Computer and AI, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Sciences and Techniques, Hassan 1st University, Settat, Morocco

    Abdelkrim Haqiq

  3. College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia

    Ahmad Taher Azar

  4. Department of Computer Science, University of South Dakota, Vermillion, SD, USA

    KC Santosh

  5. Vardhaman College of Engineering, Hyderabad, Telangana, India

    M. A. Jabbar

  6. Department of Electronics and Computer Science, Koszalin University of Technology, Koszalin, Poland

    Adam Słowik

  7. Department of Computer Science, Avinashilingam University for Women, Coimbatore, Tamil Nadu, India

    Parthasarathy Subashini

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Rather, I.A., Kumar, G., Saha, R., Kim, Th. (2023). Enhancement of Security in GFDM Using Ebola-Optimized Joint Secure Compressive Sensing Encryption and Symbol Scrambling Model. In: Hassanien, A.E., et al. The 3rd International Conference on Artificial Intelligence and Computer Vision (AICV2023), March 5–7, 2023. AICV 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 164. Springer, Cham. https://doi.org/10.1007/978-3-031-27762-7_49

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