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المؤلفQiu, Jing
المؤلفDu, Lei
المؤلفChen, Yuanyuan
المؤلفTian, Zhihong
المؤلفDu, Xiaojiang
المؤلفGuizani, Mohsen
تاريخ الإتاحة2022-11-27T11:27:59Z
تاريخ النشر2020-09-01
اسم المنشورIEEE Vehicular Technology Magazine
المعرّفhttp://dx.doi.org/10.1109/MVT.2020.3002487
الاقتباسQiu, 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.‏
الرقم المعياري الدولي للكتاب15566072
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85090204345&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/36726
الملخصWith 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.
راعي المشروعThis 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)
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوع5G mobile communication systems
العنوانArtificial Intelligence Security in 5G Networks: Adversarial Examples for Estimating a Travel Time Task
النوعArticle
الصفحات95-100
رقم العدد3
رقم المجلد15
dc.accessType Abstract Only


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