Secure Target-Tracking by UAVs in O-RAN Environment

The paper presents a comprehensive investigation into a secure target-tracking system employing Unmanned Aerial Vehicles (UAVs) within urban environments. We introduce the Enhanced Multi-Agent Q-Learning (E-MAQL) algorithm designed to enhance target-tracking accuracy while minimizing energy consumption by UAVs. Additionally, we propose a robust security framework leveraging Deep Q-Networks (DQN) for Intrusion Detection Systems (IDS), alongside the implementation of Advanced Encryption Standard (AES) and Lightweight AES (LW-AES) protocols to ensure secure communication within the Open Radio Access Network (O-RAN) architecture. Our evaluations validate the effectiveness of E-MAQL in improving tracking performance and reducing energy consumption, while the proposed security framework demonstrates promising results in detecting and mitigating potential security threats within O-RAN-based systems. Furthermore, we measured the False Positive Ratio (FPR) of the IDS at 6%. Notably, our security framework significantly enhances the target-tracking system's accuracy by 33% when exposed to false injection data attacks, elevating accuracy from 53% to 86%.