Scholarlink

This proposal introduces an innovative approach to achieving zero-trust anti-eavesdropping and anti-jamming capabilities in hierarchical drone swarms by employing reinforcement learning (RL) for boosting the potential of telecom-tuned on-device large language models (LLMs). We propose fine-tuning of on-device LLMs based on multi-modal data, including telecom-specific optimization algorithms for autonomous interference/jamming mitigation and anti-eavesdropping measures. To address the lack of emergent behavior in LLMs considering distributed collective intelligence paradigm, we propose a multi-agent RL (MARL) based approach to adaptively optimize policies based on real-time interactions. By treating the swarm of UXVs (i.e. drones) as ultra-dense networks (UDNs) with heterogeneous nodes such as fog or edge drones, we leverage MARL to exploit the inherent interference through optimum node association, resource block selection, and beam/power adjustment at each node. This efficiently mitigates interference at legitimate nodes while enhancing it elsewhere, adhering to a zero-trust approach by assuming eavesdroppers can be located anywhere. We also introduce the concept of rechargeable jamming mines (RJMs) onboard daughter or multi-role (edge) drones, which harvest energy from ambient radio frequency in the UDN environment. These RJMs are deployed and activated at strategic locations to create secure zones, maximizing the secure area around the drones by generating interference that disadvantages eavesdroppers, even with superior channel conditions. Our proposed approach has proven effective in typical UDN scenarios, where the MARL approach is used to optimize configuration settings at each base station, significantly enhancing secure area coverage. Furthermore, the solution is extendable for autonomous anti-jamming capabilities, allowing dynamic channel switching or transmission rate adaptation to mitigate jamming or interference. Our work aligns with the GENZERO24 vision of autonomous secure drone communication, offering robust protection against evolving threats and enhancing operational integrity in complex dynamic environments.

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RL-Enhanced LLMs and Rechargeable Jamming Mines: Achieving Zero-Trust Security for Hierarchical Drone Swarms

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

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  • Muhammad Shahzad Arif,
  • Sami Muhaidat,
  • Paschalis C. Sofotasios

摘要

This proposal introduces an innovative approach to achieving zero-trust anti-eavesdropping and anti-jamming capabilities in hierarchical drone swarms by employing reinforcement learning (RL) for boosting the potential of telecom-tuned on-device large language models (LLMs). We propose fine-tuning of on-device LLMs based on multi-modal data, including telecom-specific optimization algorithms for autonomous interference/jamming mitigation and anti-eavesdropping measures. To address the lack of emergent behavior in LLMs considering distributed collective intelligence paradigm, we propose a multi-agent RL (MARL) based approach to adaptively optimize policies based on real-time interactions. By treating the swarm of UXVs (i.e. drones) as ultra-dense networks (UDNs) with heterogeneous nodes such as fog or edge drones, we leverage MARL to exploit the inherent interference through optimum node association, resource block selection, and beam/power adjustment at each node. This efficiently mitigates interference at legitimate nodes while enhancing it elsewhere, adhering to a zero-trust approach by assuming eavesdroppers can be located anywhere. We also introduce the concept of rechargeable jamming mines (RJMs) onboard daughter or multi-role (edge) drones, which harvest energy from ambient radio frequency in the UDN environment. These RJMs are deployed and activated at strategic locations to create secure zones, maximizing the secure area around the drones by generating interference that disadvantages eavesdroppers, even with superior channel conditions. Our proposed approach has proven effective in typical UDN scenarios, where the MARL approach is used to optimize configuration settings at each base station, significantly enhancing secure area coverage. Furthermore, the solution is extendable for autonomous anti-jamming capabilities, allowing dynamic channel switching or transmission rate adaptation to mitigate jamming or interference. Our work aligns with the GENZERO24 vision of autonomous secure drone communication, offering robust protection against evolving threats and enhancing operational integrity in complex dynamic environments.