A swarm anomaly detection model for IoT UAVs based on a multi-modal denoising autoencoder and federated learning

•A loss function called NMSE was designed to fit the difference between clean and noisy UAV Intrusion Traffic/fault data.•A multimodal denoising autoencoder has been designed to achieve multi-source heterogeneous sensor noise removal.•An anomaly detection model for UAV swarms based on federated lear...

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Veröffentlicht in:Computers & industrial engineering Jg. 196; S. 110454
Hauptverfasser: Lu, Yu, Yang, Tao, Zhao, Chong, Chen, Wen, Zeng, Rong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.10.2024
Schlagworte:
Denoising autoencoder
Federated learning
Intrusion detection
UAV swarm
Denoising autoencoder
Federated learning
UAV swarm
Intrusion detection
ISSN:0360-8352
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Zusammenfassung:•A loss function called NMSE was designed to fit the difference between clean and noisy UAV Intrusion Traffic/fault data.•A multimodal denoising autoencoder has been designed to achieve multi-source heterogeneous sensor noise removal.•An anomaly detection model for UAV swarms based on federated learning mechanism is implemented.•Results on five datasets show that our model outperforms similar models in terms of accuracy. The widespread application of unmanned aerial vehicle (UAV) swarms has posed unique challenges for anomaly detection. Multi-modal noise from multi-source heterogeneous sensors during UAV swarm communication affects data quality, and limited data sharing between different UAV organisations restricts training a unified anomaly detection model. To address these problems, this study proposes a UAV swarm anomaly detection model based on a multi-modal denoising autoencoder and federated learning (L-MDAE). First, L-MDAE simulates noise by adding perturbations to the original data during UAV swarm communication. Second, according to the characteristics of UAV data noise, this study designs a new MSE loss function (normalised mean square error, NMSE) based on the normalised correlation coefficient. Furthermore, heterogeneous neural networks with NMSE are constructed to enhance the multi-modal noise–removal capability of the model. Finally, this study considers the UAV control node as the client and the ground control station as the server. Using a federated learning mechanism, L-MDAE is trained on a client dataset, and its parameters are integrated and distributed on the server. In this way, each UAV can effectively detect abnormal data using L-MDAE. Experimental results on five datasets, including ALFA, TLM and ITS, demonstrate that L-MDAE outperforms baseline and related models. When using ALFA, L-MDAE achieved an accuracy of 0.9919 and a swarm anomaly detection accuracy of 0.9901, approximately 2% higher than that of the baseline model.
ISSN:0360-8352
DOI:10.1016/j.cie.2024.110454