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

• URL: http://arxiv.org/abs/2501.11068v1
• Date: Sun, 19 Jan 2025 15:05:03 GMT
• Title: Generative AI-driven Cross-layer Covert Communication: Fundamentals, Framework and Case Study
• Authors: Tianhao Liu, Jiqiang Liu, Tao Zhang, Jian Wang, Jiacheng Wang, Jiawen Kang, Dusit Niyato, Shiwen Mao,
• Abstract summary: Cross-layer covert communication mechanism emerges as an effective strategy to mitigate regulatory challenges. We propose an end-to-end cross-layer covert communication scheme driven by Generative Artificial Intelligence (GenAI) Case study is conducted using diffusion reinforcement learning to sovle cloud edge internet of things cross-layer secure communication.
• Score: 62.5909195375364
• License:
• 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.

Related papers

• VENENA: A Deceptive Visual Encryption Framework for Wireless Semantic Secrecy [11.556652571936633]
  Eavesdropping has been a long-standing threat to the security and privacy of wireless communications. We propose a novel framework called Visual ENcryption for Eavesdropping NegAtion (VENENA) It combines the techniques of PLD, visual encryption, and image poisoning, into a comprehensive mechanism for secure semantic transmission.
  arXiv  Detail & Related papers  (2025-01-18T08:40:02Z)
• Toward Mixture-of-Experts Enabled Trustworthy Semantic Communication for 6G Networks [82.3753728955968]
  We introduce a novel Mixture-of-Experts (MoE)-based SemCom system. This system comprises a gating network and multiple experts, each specializing in different security challenges. The gating network adaptively selects suitable experts to counter heterogeneous attacks based on user-defined security requirements. A case study in vehicular networks demonstrates the efficacy of the MoE-based SemCom system.
  arXiv  Detail & Related papers  (2024-09-24T03:17:51Z)
• Semantic Communication Networks Empowered Artificial Intelligence of Things [2.590720801978138]
  This paper presents a comprehensive survey of security and privacy threats across various layers of semantic communication systems. We identify critical open issues in this burgeoning field warranting further investigation.
  arXiv  Detail & Related papers  (2024-07-04T14:39:28Z)
• Securing Hybrid Wireless Body Area Networks (HyWBAN): Advancements in Semantic Communications and Jamming Techniques [2.1676500745770544]
  This paper explores novel strategies to strengthen the security of Hybrid Wireless Body Area Networks (HyWBANs) Recognizing the vulnerability of HyWBAN to sophisticated cyber-attacks, we propose an innovative combination of semantic communications and jamming receivers. Our approach addresses the primary security concerns and sets the baseline for future secure biomedical communication systems advancements.
  arXiv  Detail & Related papers  (2024-04-24T18:21:08Z)
• Differentiated Security Architecture for Secure and Efficient Infotainment Data Communication in IoV Networks [55.340315838742015]
  Negligence on the security of infotainment data communication in IoV networks can unintentionally open an easy access point for social engineering attacks. In particular, we first classify data communication in the IoV network, examine the security focus of each data communication, and then develop a differentiated security architecture to provide security protection on a file-to-file basis.
  arXiv  Detail & Related papers  (2024-03-29T12:01:31Z)
• Covert Communication for Untrusted UAV-Assisted Wireless Systems [1.2190851745229392]
  UAV-assisted covert communication is a supporting technology for improving covert performances. This paper investigates the performance of joint covert and security communication in a tow-hop UAV-assisted wireless system.
  arXiv  Detail & Related papers  (2024-03-14T15:17:56Z)
• Will 6G be Semantic Communications? Opportunities and Challenges from Task Oriented and Secure Communications to Integrated Sensing [49.83882366499547]
  This paper explores opportunities and challenges of task (goal)-oriented and semantic communications for next-generation (NextG) networks through the integration of multi-task learning. We employ deep neural networks representing a dedicated encoder at the transmitter and multiple task-specific decoders at the receiver. We scrutinize potential vulnerabilities stemming from adversarial attacks during both training and testing phases.
  arXiv  Detail & Related papers  (2024-01-03T04:01:20Z)
• Generative AI-aided Joint Training-free Secure Semantic Communications via Multi-modal Prompts [89.04751776308656]
  This paper proposes a GAI-aided SemCom system with multi-model prompts for accurate content decoding. In response to security concerns, we introduce the application of covert communications aided by a friendly jammer.
  arXiv  Detail & Related papers  (2023-09-05T23:24:56Z)
• SemProtector: A Unified Framework for Semantic Protection in Deep Learning-based Semantic Communication Systems [51.97204522852634]
  We present a unified framework that aims to secure an online semantic communications system with three semantic protection modules. Specifically, these protection modules are able to encrypt semantics to be transmitted by an encryption method, mitigate privacy risks from wireless channels by a perturbation mechanism, and calibrate distorted semantics at the destination. Our framework enables an existing online SC system to dynamically assemble the above three pluggable modules to meet customized semantic protection requirements.
  arXiv  Detail & Related papers  (2023-09-04T06:34:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.