LLM-Powered Intrusion Detection Systems for Unmanned Aerial Vehicles

Abstract

The rapid proliferation of Unmanned Aerial Vehicles (UAVs) in various applications has raised significant security concerns, necessitating robust intrusion detection systems. This paper introduces a novel LLM-powered intrusion detection framework specifically designed for UAV communications. Our approach leverages Large Language Models to analyze network traffic patterns and detect sophisticated attacks that traditional methods may miss. By combining the contextual understanding capabilities of LLMs with domain-specific knowledge of UAV communication protocols, we achieve superior detection performance while maintaining low computational overhead suitable for resource-constrained UAV systems.

Key Contributions

1. LLM-Based Detection: First application of Large Language Models for UAV intrusion detection
2. UAV-Specific Framework: Tailored approach considering unique characteristics of UAV communications
3. Efficient Design: Low computational overhead suitable for resource-constrained environments
4. Superior Performance: Enhanced detection accuracy for sophisticated attacks
5. Real-world Validation: Comprehensive evaluation on actual UAV network traffic data

Method Overview

• LLM Integration: Leveraging contextual understanding of Large Language Models
• Domain Adaptation: Incorporating UAV-specific communication patterns
• Multi-stage Detection: Hierarchical analysis for efficient threat identification
• Real-time Processing: Optimized for low-latency detection requirements

Keywords

UAV Security, Intrusion Detection, Large Language Models, Network Security, Unmanned Aerial Vehicles