Generative AI-driven Cross-layer Covert Communication: Fundamentals, Framework and Case Study

2025-01-19

AI Summary

To address the challenges of cross-layer covert communication and high detectability in military networks, this paper proposes the first generative AI (GenAI)-driven dynamic cross-layer steganographic framework, supporting three representative scenarios: direct communication, private-network communication, and public-network communication. The method innovatively integrates semantic encoding, channel-adaptive artificial noise injection, and private-network scheduling to enable dynamic evolution of steganographic imperceptibility in response to real-time channel conditions. At the system level, it synergistically combines diffusion models, reinforcement learning, and multi-layer protocol optimization. Evaluated on a cloud-edge collaborative IoT testbed, the framework achieves a 3.2× improvement in communication covertness, a false positive detection rate below 0.8%, and end-to-end latency ≤120 ms—significantly outperforming state-of-the-art steganographic approaches.

Technology Category

• Multiagent Systems: Agent Communication
• Natural Language Processing: Generation
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements

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: device-to-device, private network communication, and public network communication, and analyze their application scopes. Furthermore, we propose an end-to-end cross-layer covert communication scheme driven by Generative Artificial Intelligence (GenAI), highlighting challenges and their solutions. Additionally, a case study is conducted using diffusion reinforcement learning to sovle cloud edge internet of things cross-layer secure communication.

Problem

• Stealth Communication
• Military Networks
• Information Security

Innovation

• AI-assisted covert communication
• cross-layer military networks
• security enhancement