This study presents GIIDS-AR, an enhanced version of the Generalized Intelligent Intrusion Detection System (GIIDS), developed to enhance robustness and secure diverse Unmanned Aerial Vehicles (UAVs) in Urban Air Mobility (UAM) while preserving generalization. As UAVs grow vital in logistics, emergency response, and disaster relief, their reliance on wireless communication increases exposure to cyber threats. GIIDS leverages machine learning for cross-platform detection but remains vulnerable to adversarial machine learning (AML) attacks. To assess this, GIIDS was tested under black-box, white-box, and transfer attacks. Accuracy dropped to 72 % under black-box and recall to 49.9 % under white-box settings. Adversarial training restored original performance improving accuracy to 99.0 % and F1 to 99.8 %, with AUC reaching 1.00. These evaluations were conducted using cross-dataset splits of live and simulated UAV telemetry, ensuring resilience on previously unseen data. GIIDS-AR retains layered modeling, time-aware feature encoding, and ensemble learning, while incorporating adversarial examples to improve resilience. It demonstrates strong detection performance under diverse attacks and generalizes effectively across heterogeneous UAV platforms. Our findings reveal that generalization techniques inherently contribute to adversarial robustness, positioning GIIDS-AR as one of the first unified UAV IDS frameworks capable of securing UAV networks against evolving cyber threats