Hamda Bouzabia, Tri Nhu Do
and Georges Kaddoum
Article (2023)
Document published while its authors were not affiliated with Polytechnique Montréal
An external link is available for this item
| PolyPublie URL: | https://publications.polymtl.ca/56316/ |
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| Journal Title: | IEEE Systems Journal (vol. 17, no. 2) |
| Publisher: | IEEE |
| DOI: | 10.1109/jsyst.2022.3180481 |
| Official URL: | https://doi.org/10.1109/jsyst.2022.3180481 |
| Date Deposited: | 02 Nov 2023 15:35 |
| Last Modified: | 08 Apr 2025 07:25 |
| Cite in APA 7: | Bouzabia, H., Do, T. N., & Kaddoum, G. (2023). Deep Learning-Enabled Deceptive Jammer Detection for Low Probability of Intercept Communications. IEEE Systems Journal, 17(2), 2166-2177. https://doi.org/10.1109/jsyst.2022.3180481 |
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