Adversarial Attack and Defense on Deep Learning for Air Transportation Communication Jamming

Liu, M.; Zhang, Z.; Chen, Y.; Ge, J.; Zhao, N.

Abstract

Air transportation communication jamming recognition model based on deep learning (DL) can quickly and accurately identify and classify communication jamming, to improve the safety and reliability of air traffic. However, due to the vulnerability of deep learning, the jamming recognition model can be easily attacked by the attacker’s carefully designed adversarial examples. Although some defense methods have been proposed, they have strong pertinence to attacks. Thus, new attack methods are needed to improve the defense performance of the model. In this work, we improve the existing attack methods and propose a double level attack method. By constructing the dynamic iterative step size and analyzing the class characteristics of the signals, this method can use the adversarial losses of feature layer and decision layer to generate adversarial examples with stronger attack performance. In order to improve the robustness of the recognition model, we use adversarial examples to train the model, and transfer the knowledge learned from the model to the jamming recognition models in other wireless communication environments by transfer learning. Simulation results show that the proposed attack and defense methods have good performance.

Citation

Liu, M., Zhang, Z., Chen, Y., Ge, J., & Zhao, N. (2023). Adversarial Attack and Defense on Deep Learning for Air Transportation Communication Jamming. IEEE Transactions on Intelligent Transportation Systems, https://doi.org/10.1109/tits.2023.3262347

• Journal Article Type: Article
• Acceptance Date: Mar 24, 2023
• Online Publication Date: Apr 4, 2023
• Publication Date: 2023
• Deposit Date: Mar 29, 2023
• Publicly Available Date: Mar 29, 2023
• Journal: IEEE Transactions on Intelligent Transportation Systems
• Print ISSN: 1524-9050
• Electronic ISSN: 1558-0016
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1109/tits.2023.3262347
• Public URL: https://durham-repository.worktribe.com/output/1175868