🤖 AI Summary
To address the asymmetry between attacker and defender inherent in physical-layer security (PLS)’s passive defense paradigm, this paper proposes a Physical-Layer Deception (PLD) framework. PLD actively injects semantically consistent deceptive messages into eavesdroppers’ observations via a two-stage encoding scheme that integrates randomized cipher coding with non-orthogonal multiple access (NOMA). Crucially, PLD guarantees information-theoretic ciphertext confidentiality under the weak assumption that the legitimate channel quality exceeds that of the eavesdropper—even when the eavesdropper possesses identical prior knowledge as the legitimate receiver. We formally prove PLD’s security and validate its superiority through numerical simulations, demonstrating significant gains in secrecy rate and robustness against eavesdropping compared to conventional PLS schemes. This work constitutes the first systematic introduction of active deception mechanisms into physical-layer security, thereby relaxing the stringent channel-condition requirements imposed by classical PLS approaches.
📝 Abstract
Physical layer security (PLS) is a promising technology to secure wireless communications by exploiting the physical properties of the wireless channel. However, the passive nature of PLS creates a significant imbalance between the effort required by eavesdroppers and legitimate users to secure data. To address this imbalance, in this article, we propose a novel framework of physical layer deception (PLD), which combines PLS with deception technologies to actively counteract wiretapping attempts. Combining a two-stage encoder with randomized ciphering and non-orthogonal multiplexing, the PLD approach enables the wireless communication system to proactively counter eavesdroppers with deceptive messages. Relying solely on the superiority of the legitimate channel over the eavesdropping channel, the PLD framework can effectively protect the confidentiality of the transmitted messages, even against eavesdroppers who possess knowledge equivalent to that of the legitimate receiver. We prove the validity of the PLD framework with in-depth analyses and demonstrate its superiority over conventional PLS approaches with comprehensive numerical benchmarks.