Physical (PHY) Layer Security
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Traditional cryptography methods rely on computational complexity.
DNN in PHY-layer Security
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Proposed a deep-learning-based PHY-layer authentication:
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Three gradient descent methods are adopted to increase the training speed of deep neural networks (DNN), which allow smaller computation overheads and lower energy consumption, and the authentication success rate indicates the difference performance between authentication algorithms.
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Focus on multi-user authentication scenario.
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The interaction between the edge device and mobile cloud computing server is performed after the device and server implement access authentication.
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Enhanced DNN based multi-user authentication scheme balances the edge devices, malicious ones and attackers, increase the security level of MMC system in IoT in the physical layer.
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Relay Networks Security
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Security Issues in relay networks:
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Relays are untrusted, the data transmitted in the network requires confidentiality.
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Relays are trusted, no need to encrypt the data.
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Wireless secret key agreement
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Use a secret variable to indicate the channel state.
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Allow transmission array optimization to extract keys faster.
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Expose the extraction coefficients of the multipath selection components.
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Use one-bit response in the practical network.
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Involving the cooperating relays
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Dynamic algorithm to generate a key encrypted the transmission between relays.
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Cooperative algorithm to select the relays between the source and jam relay.
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