Net-GPT: A LLM-Empowered Man-in-the-Middle Chatbot for Unmanned Aerial Vehicle

Abstract

In the dynamic realm of AI, integrating Large Language Models (LLMs) with security systems reshape cybersecurity. LLMs bolster defense against cyber threats but also introduce risks, aiding adversaries in generating malicious content, discovering vulnerabilities, and distorting perceptions. This paper presents Net-GPT, an LLM-empowered offensive chatbot that understands network protocols and launches Unmanned Aerial Vehicles (UAV)-based Man-in-the-middle (MITM) attacks against a hijack communication between UAV and Ground Control Stations (GCS). Facilitated by an edge server equipped with finely tuned LLMs, Net-GPT crafts mimicked network packets between UAV and GCS. Leveraging the adaptability of popular LLMs, Net-GPT produces context-aligned network packets. We fine-tune and assess Net-GPT's LLM-based efficacy, showing its impressive generative accuracy: 95.3% for Llama-2-13B and 94.1% for Llama-2-7B. Smaller LLMs, such as Distil-GPT-2, reach 77.9% predictive capability of Llama-2-7B but are 47× faster. Cost-efficiency tests highlight model quality's impact on accuracy while fine-tuning data quantity enhances predictability on specific metrics. It holds great potential to be used in edge-computing environments with amplified computing capability.