BPBiLSTM-IDS: a lightweight intrusion detection framework for cyber-physical UAV networks

Unmanned Aerial Vehicles (UAVs) have revolutionized modern technology by enabling autonomous operations in dynamic environments; however, their reliance on wireless networks exposes them to significant cybersecurity threats. These threats include De-authentication Denial of Service, False Data Injection (FDI), Replay, and Evil Twin attacks, which severely impact usability and data integrity. Conventional Intrusion Detection Systems (IDS) suffer from drawbacks such as high false alarm rates, excessive resource consumption, and non-proportional mechanisms for dynamic UAV topologies. To address these challenges, this study introduces an enhanced AIDS architecture in which optimal features are selected using Binary Pigeon Optimization (BP), and intrusion detection is performed using a Bidirectional Long Short-Term Memory (Bi-LSTM) with 1D-CNN model. BP enables feature selection independent of computational cost, mitigating the impact of high-cost or exhaustive features, while Bi-LSTM effectively captures temporal characteristics of UAV network traffic for accurate attack detection. Experimental evaluation on a cyber-physical UAV dataset demonstrates that the proposed BP + Bi-LSTM model along with 1D-CNN outperforms traditional ML approaches such as Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Deep Neural Network (DNN), achieving an accuracy of 98.74% ± 0.07 (mean ± std over 10 runs), along with high precision, recall, and an optimal false positive rate. These results confirm that the proposed model is a scalable, adaptive, and lightweight solution for real-time intrusion detection in UAV networks.