Jammer Detection based on Artificial Neural Networks-Reference-Cited by-同舟云学术

Jammer Detection based on Artificial Neural Networks

Published:2019 Issue: Volume: Page:
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Container-title:Proceedings of the ACM Workshop on Wireless Security and Machine Learning - WiseML 2019
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Author:

Gecgel Selen¹,Goztepe Caner¹,Kurt Gunes Karabulut¹

Affiliation:

1. Istanbul Technical University, Wireless Communication Research Laboratory, Istanbul, Turkey

Publisher

ACM Press

Reference11 articles.

1. A. El-Keyi, O. Üreten, H. Yanikomeroglu, and T. Yensen, "LTE for public safety networks: Synchronization in the presence of jamming," IEEE Access, vol. 5, pp. 20 800--20 813, 2017.

2. C. Shahriar, R. McGwier, and T. C. Clancy, "Performance impact of pilot tone randomization to mitigate OFDM jamming attacks," in 2013 IEEE 10th Consumer Comm. and Networking Conf., Jan 2013, pp. 813--816.

3. J. Vinogradova, E. Björnson, and E. G. Larsson, "Detection and mitigation of jamming attacks in massive mimo systems using random matrix theory," in 2016 IEEE 17th Int. Workshop on Sig. Process. Adv. in Wireless Comm., July 2016, pp. 1--5.

4. M. Spuhler, D. Giustiniano, V. Lenders, M. Wilhelm, and J. B. Schmitt, "Detection of reactive jamming in DSSS-based wireless communications," IEEE Trans. on Wireless Comm., vol. 13, no. 3, pp. 1593--1603, March 2014.

5. D. Karagiannis and A. Argyriou, "Jamming attack detection in a pair of RF communicating vehicles using unsupervised machine learning," Vehicular Communications, vol. 13, pp. 56--63, 2018.

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