A Semantic Model for Physical Layer Deception

Physical layer deception (PLD) is a novel security mechanism that combines physical layer security (PLS) with deception technologies to actively defend against eavesdroppers. In this paper, we establish a novel semantic model for PLD that evaluates its performance in terms of semantic distortion. By analyzing semantic distortion at varying levels of knowledge on the receiver's part regarding the key, we derive the receiver's optimal decryption strategy, and consequently, the transmitter's optimal deception strategy. The proposed semantic model provides a more generic understanding of the PLD approach independent from coding or multiplexing schemes, and allows for efficient real-time adaptation to fading channels.

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Publication Details

Published
2025-06-08
DOI
https://doi.org/10.1109/icc52391.2025.11162021
Citations
1
Primary Topic
Advanced Malware Detection Techniques
Type
article
Field-Weighted Citation Impact
1.49
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article

A Semantic Model for Physical Layer Deception

Hans D. Schotten, Bin Han, Giuseppe Caire, Anke Schmeink et al.
1 citations
Advanced Malware Detection Techniques
1.49
article

A Semantic Model for Physical Layer Deception

Hans D. Schotten, Bin Han, Giuseppe Caire, Anke Schmeink, Yao Zhu
article en
1 citations

Abstract

Physical layer deception (PLD) is a novel security mechanism that combines physical layer security (PLS) with deception technologies to actively defend against eavesdroppers. In this paper, we establish a novel semantic model for PLD that evaluates its performance in terms of semantic distortion. By analyzing semantic distortion at varying levels of knowledge on the receiver's part regarding the key, we derive the receiver's optimal decryption strategy, and consequently, the transmitter's optimal deception strategy. The proposed semantic model provides a more generic understanding of the PLD approach independent from coding or multiplexing schemes, and allows for efficient real-time adaptation to fading channels.

University of Kaiserslautern (DE), Technische Universität Berlin (DE), University of Applied Sciences Kaiserslautern (DE), RWTH Aachen University (DE)
Openalex Percentile: Top 14%
Advanced Malware Detection Techniques
1.49
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