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Physical Layer Authentication in the Internet of Things based on Deep Learning

Abdollahi, Majid | 2020

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 53479 (19)
  4. University: Sharif University of Technology
  5. Department: Materials Science and Engineering
  6. Advisor(s): Behroozi, Hamid
  7. Abstract:
  8. IoT security is an important step in the rapid development of its applications and ser-vices. Many security protocols have been defined to maintain security at higher communication layers, such as the transport layer and the application layer, but with the advent of quantum computers due to high-speed parallel computing, the previous methods are no longer secure and we have to establish security at lower layers such as the physical layer. The efficiency of IoT devices depends on the reliability of their message transmission. Cyber-attacks such as data injection, eavesdropping and man-in-the-middle can lead to security challenges. In this study, we propose a new deep learning based component for IoT signal authentication that detects cyber attacks. The proposed component is based on the generative adversarial network and one of its types is called the energy-based generative adversarial network. In previous studies, the watermarking method is proposed. This method allows IoT signal sender to extract specific features of their own generated signal and dynamically watermark these features in the final generated signal and send it to the receiver. This method allows the IoT signal receiver, to verify the reliability of the signals. It also avoids complex attack scenarios such as eavesdropping in which the attacker collects data from IoT devices and intends to break the watermarking algorithm, but there is dis-advantages such as delays in the receiver’s authentication and common sender and receiver key that we can fix them by using the proposed component
  9. Keywords:
  10. Authentication ; Internet of Things ; Deep Learning ; Physical Layer ; Internet Protocol Security

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