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AuthorQiu, Jing
AuthorDu, Lei
AuthorChen, Yuanyuan
AuthorTian, Zhihong
AuthorDu, Xiaojiang
AuthorGuizani, Mohsen
Available date2022-11-27T11:27:59Z
Publication Date2020-09-01
Publication NameIEEE Vehicular Technology Magazine
Identifierhttp://dx.doi.org/10.1109/MVT.2020.3002487
CitationQiu, J., Du, L., Chen, Y., Tian, Z., Du, X., & Guizani, M. (2020). Artificial intelligence security in 5G networks: Adversarial examples for estimating a travel time task. IEEE Vehicular Technology Magazine, 15(3), 95-100.‏
ISSN15566072
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85090204345&origin=inward
URIhttp://hdl.handle.net/10576/36726
AbstractWith the rapid development of the Internet, the nextgeneration network (5G) has emerged. 5G can support a variety of new applications, such as the Internet of Things (IoT), virtual reality (VR), and the Internet of Vehicles. Most of these new applications depend on deep learning algorithms, which have made great advances in many areas of artificial intelligence (AI). However, researchers have found that AI algorithms based on deep learning pose numerous security problems. For example, deep learning is susceptible to a well-designed input sample formed by adding small perturbations to the original sample. This well-designed input with small perturbations, which are imperceptible to humans, is called an adversarial example. An adversarial example is similar to a truth example, but it can render the deep learning model invalid. In this article, we generate adversarial examples for spatiotemporal data. Based on the travel time estimation (TTE) task, we use two methods-white-box and blackbox attacks-to invalidate deep learning models. Experiment results show that the adversarial examples successfully attack the deep learning model and thus that AI security is a big challenge of 5G.
SponsorThis research is supported by the Guangdong Province Key Research and Development Plan (grant 2019B010137004); National Key Research and Development Plan (grant 2018YFB0803504); National Natural Science Foundation of China (grants 61871140, 61872100, and U1636215)
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Subject5G mobile communication systems
TitleArtificial Intelligence Security in 5G Networks: Adversarial Examples for Estimating a Travel Time Task
TypeArticle
Pagination95-100
Issue Number3
Volume Number15
dc.accessType Abstract Only


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