Generative AI-driven Cross-layer Covert Communication: Fundamentals, Framework and Case Study
Tianhao Liu, Jiqiang Liu, Tao Zhang, Jian Wang, Jiacheng Wang, Jiawen, Kang, Dusit Niyato, Shiwen Mao

TL;DR
This paper explores a novel cross-layer covert communication framework driven by Generative AI, addressing security challenges in military networks through a comprehensive survey, a proposed scheme, and a case study using diffusion reinforcement learning.
Contribution
It introduces a new AI-driven cross-layer covert communication scheme and provides a case study demonstrating its application in secure IoT communications.
Findings
Surveyed existing covert communication scenarios and their applications.
Proposed an end-to-end AI-driven covert communication framework.
Conducted a case study using diffusion reinforcement learning for secure IoT communication.
Abstract
Ensuring end-to-end cross-layer communication security in military networks by selecting covert schemes between nodes is a key solution for military communication security. With the development of communication technology, covert communication has expanded from the physical layer to the network and application layers, utilizing methods such as artificial noise, private networks, and semantic coding to transmit secret messages. However, as adversaries continuously eavesdrop on specific communication channels, the accumulation of sufficient data may reveal underlying patterns that influence concealment, and establishing a cross-layer covert communication mechanism emerges as an effective strategy to mitigate these regulatory challenges. In this article, we first survey the communication security solution based on covert communication, specifically targeting three typical scenarios:…
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Taxonomy
TopicsAdvanced Steganography and Watermarking Techniques · Internet Traffic Analysis and Secure E-voting · Digital Media Forensic Detection
