Citation

Attaullah, Hafiz Muhammad and Khan, Inam Ullah and Alam, Muhammad Mansoor and Mohd Su'ud, Mazliham and Kaushik, Keshav (2026) RoboLSTM-IDS: multi-dataset evaluation of deep learning framework for UAV network. PeerJ Computer Science, 12. e3500. ISSN 2376-5992

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Abstract

The growing deployment of uncrewed aerial vehicles (UAV) in autonomous and networked missions has heightened their exposure to both cyber and cyber-physical attacks, underscoring the need for intelligent and lightweight intrusion detection systems (IDS) solutions. This study introduces RoboLSTM-IDS, a deep anomaly-based framework that combines robust feature engineering with temporal sequence modeling for UAV network security. Leveraging Robust Optimization-Based Tabular Feature Engineering (ROBOTa), a robust optimization-based feature selection technique—the system extracts stable, high-impact features from complex UAV telemetry and communication data. These are modeled using a Long Short-Term Memory network to capture sequential attack dynamics. Comprehensive experiments conducted on five benchmark datasets, including real-world UAV cyber-physical data (T-ITS), CICIDS-2017, UNSW-NB15, and their CTGAN-augmented variants, demonstrate that RoboLSTM-IDS consistently outperforms traditional machine learning and deep learning baselines. It achieves up to 99.62% accuracy and 0.997 AUC, while maintaining low false positive rates and real-time execution performance. Unlike conventional IDS models that are computationally heavy, proposed model achieves a 6× smaller model size, 3× lower memory footprint, and significantly reduced inference latency. These results confirm RoboLSTM-IDS as an effective and scalable IDS solution tailored for next-generation UAV ecosystems.

Item Type:	Article
Uncontrolled Keywords:	UAV, IDS, anomaly detection, roboLSTM
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	03 Apr 2026 03:56
Last Modified:	03 Apr 2026 03:56
URII:	http://shdl.mmu.edu.my/id/eprint/15692

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