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Physical Layer Deception based on Semantic Distortion

Wenwen Chen, Bin Han, Yao Zhu, Anke Schmeink, Giuseppe Caire, Hans D. Schotten

TL;DR

The paper addresses securing wireless transmissions by extending physical layer deception to a semantic-security setting, introducing a two-channel PLD model and a semantic distortion metric $D$. It develops convexity results for Eve’s decryption strategies, decomposes the optimization into initial resource allocation, ciphering-probability tuning, and adaptive allocation, and provides a low-complexity algorithm with closed-form solutions across nine strategy cases. Numerical results validate convexity, demonstrate the effectiveness of the optimization, and reveal periodic patterns in the strategic choices of Eve and Bob under iterative updates. The work offers a practical, attack-tolerant approach to maximize Eve’s distortion while preserving legitimate communication, with implications for robust deceptions-aware wireless security.

Abstract

Physical layer deception (PLD) is a framework we previously introduced that integrates physical layer security (PLS) with deception techniques, enabling proactive countermeasures against eavesdropping rather than relying solely on passive defense. We extend this framework to a semantic communication model and conduct a theoretical analysis using semantic distortion as the performance metric. In this work, we further investigate the receiver's selection of decryption strategies and the transmitter's optimization of encryption strategies. By anticipating the decryption strategy likely to be employed by the legitimate receiver and eavesdropper, the transmitter can optimize resource allocation and encryption parameters, thereby maximizing the semantic distortion at the eavesdropper while maintaining a low level of semantic distortion for the legitimate receiver. We present a rigorous analysis of the resulting optimization problem, propose an efficient optimization algorithm, and derive closed-form optimal solutions for multiple scenarios. Finally, we corroborate the theoretical findings with numerical simulations, which also confirm the practicality of the proposed algorithm.

Physical Layer Deception based on Semantic Distortion

TL;DR

The paper addresses securing wireless transmissions by extending physical layer deception to a semantic-security setting, introducing a two-channel PLD model and a semantic distortion metric . It develops convexity results for Eve’s decryption strategies, decomposes the optimization into initial resource allocation, ciphering-probability tuning, and adaptive allocation, and provides a low-complexity algorithm with closed-form solutions across nine strategy cases. Numerical results validate convexity, demonstrate the effectiveness of the optimization, and reveal periodic patterns in the strategic choices of Eve and Bob under iterative updates. The work offers a practical, attack-tolerant approach to maximize Eve’s distortion while preserving legitimate communication, with implications for robust deceptions-aware wireless security.

Abstract

Physical layer deception (PLD) is a framework we previously introduced that integrates physical layer security (PLS) with deception techniques, enabling proactive countermeasures against eavesdropping rather than relying solely on passive defense. We extend this framework to a semantic communication model and conduct a theoretical analysis using semantic distortion as the performance metric. In this work, we further investigate the receiver's selection of decryption strategies and the transmitter's optimization of encryption strategies. By anticipating the decryption strategy likely to be employed by the legitimate receiver and eavesdropper, the transmitter can optimize resource allocation and encryption parameters, thereby maximizing the semantic distortion at the eavesdropper while maintaining a low level of semantic distortion for the legitimate receiver. We present a rigorous analysis of the resulting optimization problem, propose an efficient optimization algorithm, and derive closed-form optimal solutions for multiple scenarios. Finally, we corroborate the theoretical findings with numerical simulations, which also confirm the practicality of the proposed algorithm.
Paper Structure (22 sections, 3 theorems, 59 equations, 11 figures, 1 table, 1 algorithm)

This paper contains 22 sections, 3 theorems, 59 equations, 11 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. Problem Setup
  4. System Model
  5. Performance Metrices
  6. Decryptor Model
  7. Strategy Optimization
  8. Initial Resource Allocation Optimization
  9. Ciphering Probability Optimization
  10. Adaptive Resource Allocation Optimization
  11. Boundary for Bob's Perception Strategy
  12. Boundary for Bob's Dropping Strategy
  13. Boundary for Bob's Exclusion Strategy
  14. Optimal Point for Eve's Perception Strategy
  15. Optimal Point for Eve's Dropping Strategy
  16. ...and 7 more sections

Key Result

Theorem 1

The maximum of $D_{\mathrm{Eve}}$ is achieved at $(n_{\mathrm{M}}^{\mathrm{max}},n_{\mathrm{K}}^{\mathrm{min}})$ when $\alpha\geqslant \frac{D_{\mathrm{loss}}}{\varepsilon_{\mathrm{Eve,\mathrm{K}}}^{\mathrm{max}}D_{\mathrm{conf}}}$, while achieved at $(n_{\mathrm{M}}^{\mathrm{min}},n_{\mathrm{K}}^{\

Figures (11)

  • Figure 1: System model of pld at the transmitter side
  • Figure 2: Decryptor model at the receiver side
  • Figure 3: Dual-channel model of pld
  • Figure 4: Model of the primary transport channel
  • Figure 5: Model of the secondary transport channel
  • ...and 6 more figures

Theorems & Definitions (6)

  • Theorem 1
  • proof
  • Theorem 2
  • proof
  • Theorem 3
  • proof