Abstract
With the rapid development of deep learning technologies, more and more DNN models are employed in wireless communication tasks. While DNNs improve the service, they also incur potential security threats from malicious users. Adversarial Example is a generally discussed attack targeting the deep learning-based models, which undoubtedly threatens the security of deep learning-based wireless networks. In this paper, we widely investigate adversarial example attacks in wireless network scenarios. It helps us become aware of the adversarial example threats that DNNs used in wireless networks are exposed to, and more efforts are required to defend against these attacks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Chakraborty, A., Alam, M., Dey, V., Chattopadhyay, A., Mukhopadhyay, D.: Adversarial attacks and defences: a survey. ar**v preprint ar**v:1810.00069 (2018)
Flowers, B., Buehrer, R.M., Headley, W.C.: Evaluating adversarial evasion attacks in the context of wireless communications. IEEE Trans. Inf. Forensics Secur. 15, 1102–1113 (2019)
Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. ar**v preprint ar**v:1412.6572 (2014)
Huang, R., Xu, B., Schuurmans, D., Szepesvári, C.: Learning with a strong adversary. ar**v preprint ar**v:1511.03034 (2015)
Kim, B., Sagduyu, Y.E., Davaslioglu, K., Erpek, T., Ulukus, S.: Over-the-air adversarial attacks on deep learning based modulation classifier over wireless channels. In: 2020 54th Annual Conference on Information Sciences and Systems (CISS), pp. 1–6. IEEE (2020)
Kim, B., Sagduyu, Y.E., Davaslioglu, K., Erpek, T., Ulukus, S.: Channel-aware adversarial attacks against deep learning-based wireless signal classifiers. IEEE Trans. Wirel. Commun. (2021)
Kim, B., Sagduyu, Y.E., Erpek, T., Ulukus, S.: Adversarial attacks on deep learning based mmwave beam prediction in 5G and beyond. ar**v preprint ar**v:2103.13989 (2021)
Kim, B., Shi, Y., Sagduyu, Y.E., Erpek, T., Ulukus, S.: Adversarial attacks against deep learning based power control in wireless communications. ar**v preprint ar**v:2109.08139 (2021)
Kokalj-Filipovic, S., Miller, R., Morman, J.: Targeted adversarial examples against RF deep classifiers. In: Proceedings of the ACM Workshop on Wireless Security and Machine Learning, pp. 6–11 (2019)
Kurakin, A., Goodfellow, I., Bengio, S., et al.: Adversarial examples in the physical world (2016)
Nazir, R., Kumar, K., David, S., Ali, M., et al.: Survey on wireless network security. Archiv. Comput. Methods Eng. 1–20 (2021)
Papernot, N., McDaniel, P., Wu, X., Jha, S., Swami, A.: Distillation as a defense to adversarial perturbations against deep neural networks. In: 2016 IEEE symposium on security and privacy (SP), pp. 582–597. IEEE (2016)
Sadeghi, M., Larsson, E.G.: Adversarial attacks on deep-learning based radio signal classification. IEEE Wirel. Commun. Lett. 8(1), 213–216 (2018)
Sagduyu, Y.E., Shi, Y., Erpek, T.: IoT network security from the perspective of adversarial deep learning. In: 2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), pp. 1–9. IEEE (2019)
Sagduyu, Y.E., et al.: When wireless security meets machine learning: motivation, challenges, and research directions. ar**v preprint ar**v:2001.08883 (2020)
Yuan, X., He, P., Zhu, Q., Li, X.: Adversarial examples: attacks and defenses for deep learning. IEEE Trans. Neural Networks Learn. Syst. 30(9), 2805–2824 (2019)
Zhang, J., Li, C.: Adversarial examples: opportunities and challenges. IEEE Trans. Neural Networks Learn. Syst. 31(7), 2578–2593 (2019)
Kokalj-Filipovic, S., Miller, R., Vanhoy, G.: Adversarial examples in RF deep learning: detection and physical robustness. In: 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp. 1–5. IEEE (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Dong, J., Gong, X., Xue, M. (2022). Adversarial Examples in Wireless Networks: A Comprehensive Survey. In: Wu, K., Wang, L., Chen, Y. (eds) Edge Computing and IoT: Systems, Management and Security. ICECI 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 437. Springer, Cham. https://doi.org/10.1007/978-3-031-04231-7_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-04231-7_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-04230-0
Online ISBN: 978-3-031-04231-7
eBook Packages: Computer ScienceComputer Science (R0)