{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T02:26:30Z","timestamp":1775010390560,"version":"3.50.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T00:00:00Z","timestamp":1679443200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T00:00:00Z","timestamp":1679443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Wireless Netw"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s11276-023-03299-4","type":"journal-article","created":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T15:11:13Z","timestamp":1679497873000},"page":"2967-2980","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Adversarial defense method based on ensemble learning for modulation signal intelligent recognition"],"prefix":"10.1007","volume":"29","author":[{"given":"Chao","family":"Han","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruoxi","family":"Qin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linyuan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weijia","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6818-6441","authenticated-orcid":false,"given":"Bin","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,22]]},"reference":[{"issue":"19","key":"3299_CR1","doi-asserted-by":"publisher","first-page":"19438","DOI":"10.1109\/JIOT.2022.3167107","volume":"9","author":"C Hou","year":"2022","unstructured":"Hou, C., Liu, G., Tian, Q., Zhou, Z., Hua, L., & Lin, Y. (2022). Multi-signal modulation classification using sliding window detection and complex convolutional network in frequency domain. IEEE Internet of Things Journal, 9(19), 19438\u201319449.","journal-title":"IEEE Internet of Things Journal"},{"key":"3299_CR2","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1016\/j.ins.2022.11.076","volume":"619","author":"S Liu","year":"2023","unstructured":"Liu, S., Gao, P., Li, Y., et al. (2023). Multi-modal fusion network with complementarity and importance for emotion recognition. Information Sciences, 619, 679\u2013694.","journal-title":"Information Sciences"},{"key":"3299_CR3","doi-asserted-by":"crossref","unstructured":"Fu, X., Peng, Y., Liu, Y., Lin, Y., Gui, G., Gacanin, H., & Adachi, F. (2023). Semi-supervised specific emitter identification method using metric-adversarial training. IEEE Internet of Things Journal.","DOI":"10.1109\/JIOT.2023.3240242"},{"key":"3299_CR4","doi-asserted-by":"crossref","unstructured":"Franco, H., Cobo-Kroenke, C., Welch, S., & Graciarena, M. (2020). Wideband spectral monitoring using deep learning. In Proceedings of the 2nd ACM workshop on wireless security and machine learning (pp. 19\u201324).","DOI":"10.1145\/3395352.3402620"},{"key":"3299_CR5","doi-asserted-by":"crossref","unstructured":"Omotere, O., Fuller, J., Qian, L., & Han, Z. (2018). Spectrum occupancy prediction in coexisting wireless systems using deep learning. In: IEEE 88th vehicular technology conference (pp. 1\u20137).","DOI":"10.1109\/VTCFall.2018.8690575"},{"key":"3299_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.02.005","author":"S Liu","year":"2023","unstructured":"Liu, S., Huang, S., Wang, S., et al. (2023). Visual tracking in complex scenes: A location fusion mechanism based on the combination of multiple visual cognition flows. Information Fusion. https:\/\/doi.org\/10.1016\/j.inffus.2023.02.005","journal-title":"Information Fusion"},{"key":"3299_CR7","doi-asserted-by":"crossref","unstructured":"Xu, Z., Han, G., Liu, L., et al. (2022). A lightweight specific emitter identification model for IIoT devices based on adaptive broad learning. IEEE Transactions on Industrial Informatics.","DOI":"10.1109\/TII.2022.3206309"},{"issue":"24","key":"3299_CR8","doi-asserted-by":"publisher","first-page":"24980","DOI":"10.1109\/JIOT.2022.3194967","volume":"9","author":"Y Wang","year":"2022","unstructured":"Wang, Y., Gui, G., Lin, Y., et al. (2022). Few-shot specific emitter identification via deep metric ensemble learning. IEEE Internet of Things Journal, 9(24), 24980\u201324994.","journal-title":"IEEE Internet of Things Journal"},{"issue":"7","key":"3299_CR9","doi-asserted-by":"publisher","first-page":"7942","DOI":"10.1109\/TVT.2022.3164935","volume":"71","author":"X Fu","year":"2022","unstructured":"Fu, X., Gui, G., Wang, Y., et al. (2022). Automatic modulation classification based on decentralized learning and ensemble learning. IEEE Transactions on Vehicular Technology, 71(7), 7942\u20137946.","journal-title":"IEEE Transactions on Vehicular Technology"},{"issue":"3","key":"3299_CR10","doi-asserted-by":"publisher","first-page":"1374","DOI":"10.1109\/TCCN.2022.3169740","volume":"8","author":"X Zhang","year":"2022","unstructured":"Zhang, X., Zhao, H., Zhu, H., et al. (2022). NAS-AMR: Neural architecture search-based automatic modulation recognition for integrated sensing and communication systems. IEEE Transactions on Cognitive Communications and Networking, 8(3), 1374\u20131386.","journal-title":"IEEE Transactions on Cognitive Communications and Networking"},{"key":"3299_CR11","unstructured":"O'shea, T. J., & West, N. (2016). Radio machine learning dataset generation with GNU radio. In Proceedings of the GNU radio conference (Vol. 1, No. 1)."},{"issue":"11","key":"3299_CR12","doi-asserted-by":"publisher","first-page":"9012","DOI":"10.1109\/JIOT.2021.3120197","volume":"9","author":"Z Bao","year":"2021","unstructured":"Bao, Z., Lin, Y., Zhang, S., et al. (2021). Threat of adversarial attacks on DL-based IoT device identification. IEEE Internet of Things Journal, 9(11), 9012\u20139024.","journal-title":"IEEE Internet of Things Journal"},{"key":"3299_CR13","unstructured":"Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2013). Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199."},{"issue":"1","key":"3299_CR14","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1109\/LWC.2018.2867459","volume":"8","author":"M Sadeghi","year":"2018","unstructured":"Sadeghi, M., & Larsson, E. G. (2018). Adversarial attacks on deep-learning based radio signal classification. IEEE Wireless Communications Letters, 8(1), 213\u2013216.","journal-title":"IEEE Wireless Communications Letters"},{"key":"3299_CR15","doi-asserted-by":"crossref","unstructured":"Lin, Y., Zhao, H., Tu, Y., Mao, S., & Dou, Z. (2020). Threats of adversarial attacks in DNN-based modulation recognition. In IEEE conference on computer communications (pp. 2469\u20132478).","DOI":"10.1109\/INFOCOM41043.2020.9155389"},{"key":"3299_CR16","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1109\/TR.2022.3161138","volume":"71.2","author":"P Qi","year":"2022","unstructured":"Qi, P., Jiang, T., Wang, L., et al. (2022). Detection tolerant black-box adversarial attack against automatic modulation classification with deep learning. IEEE Transactions on Reliability, 71.2, 674\u2013686.","journal-title":"IEEE Transactions on Reliability"},{"key":"3299_CR17","doi-asserted-by":"publisher","first-page":"19295","DOI":"10.1109\/ACCESS.2018.2795803","volume":"6","author":"MM Rana","year":"2018","unstructured":"Rana, M. M., Xiang, W., Wang, E., Li, X., & Choi, B. J. (2018). Internet of Things infrastructure for wireless power transfer systems. IEEE Access, 6, 19295\u201319303.","journal-title":"IEEE Access"},{"key":"3299_CR18","doi-asserted-by":"crossref","unstructured":"Tian, Q., Zhang, S., Mao, S., et al. (2022). Adversarial attacks and defenses for digital communication signals identification. Digital Communications and Networks.","DOI":"10.1016\/j.dcan.2022.10.010"},{"issue":"6","key":"3299_CR19","doi-asserted-by":"publisher","first-page":"3868","DOI":"10.1109\/TWC.2021.3124855","volume":"21","author":"B Kim","year":"2021","unstructured":"Kim, B., Sagduyu, Y. E., Davaslioglu, K., et al. (2021). Channel-aware adversarial attacks against deep learning-based wireless signal classifiers. IEEE Transactions on Wireless Communications, 21(6), 3868\u20133880.","journal-title":"IEEE Transactions on Wireless Communications"},{"key":"3299_CR20","doi-asserted-by":"crossref","unstructured":"Kokalj-Filipovic, S., Miller, R., & Vanhoy, G. (2019). Adversarial examples in RF deep learning: Detection and physical robustness. In IEEE global conference on signal and information processing (pp. 1\u20135).","DOI":"10.1109\/GlobalSIP45357.2019.8969138"},{"key":"3299_CR21","doi-asserted-by":"crossref","unstructured":"Carlini, N., & Wagner, D. (2017). Towards evaluating the robustness of neural networks. In IEEE symposium on security and privacy (pp. 39\u201357).","DOI":"10.1109\/SP.2017.49"},{"key":"3299_CR22","doi-asserted-by":"crossref","unstructured":"Adesina, D., Hsieh, C. C., Sagduyu, Y. E., & Qian, L. (2022). Adversarial machine learning in wireless communications using RF data: A review. IEEE Communications Surveys and Tutorials.","DOI":"10.1109\/COMST.2022.3205184"},{"key":"3299_CR23","unstructured":"Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014). Explaining and harnessing adversarial examples. In International conference on learning representations."},{"key":"3299_CR24","doi-asserted-by":"crossref","unstructured":"Kurakin, A., Goodfellow, I. J., & Bengio, S. (2018). Adversarial examples in the physical world. In Artificial intelligence safety and security (pp. 99\u2013112).","DOI":"10.1201\/9781351251389-8"},{"key":"3299_CR25","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., & Vladu, A. (2017). Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083."},{"key":"3299_CR26","doi-asserted-by":"crossref","unstructured":"Dong, Y., Liao, F., Pang, T., Su, H., Zhu, J., Hu, X., & Li, J. (2018). Boosting adversarial attacks with momentum. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 9185\u20139193).","DOI":"10.1109\/CVPR.2018.00957"},{"key":"3299_CR27","unstructured":"Hinton, G., Vinyals, O., & Dean, J. (2015). Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531."},{"key":"3299_CR28","doi-asserted-by":"crossref","unstructured":"Lecuyer, M., Atlidakis, V., Geambasu, R., Hsu, D., & Jana, S. (2019). Certified robustness to adversarial examples with differential privacy. In IEEE symposium on security and privacy (pp. 656\u2013672).","DOI":"10.1109\/SP.2019.00044"},{"key":"3299_CR29","doi-asserted-by":"crossref","unstructured":"Yang, G., Duan, T., Hu, J. E., Salman, H., Razenshteyn, I., & Li, J. (2020). Randomized smoothing of all shapes and sizes. In International conference on machine learning (pp. 10693\u201310705).","DOI":"10.1109\/JIOT.2020.3048253"},{"key":"3299_CR30","unstructured":"He, W., Wei, J., Chen, X., Carlini, N., & Song, D. (2017). Adversarial example defense: Ensembles of weak defenses are not strong. In 11th USENIX workshop on offensive technologies."},{"issue":"2","key":"3299_CR31","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1023\/A:1022859003006","volume":"51","author":"LI Kuncheva","year":"2003","unstructured":"Kuncheva, L. I., & Whitaker, C. J. (2003). Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Machine learning, 51(2), 181\u2013207.","journal-title":"Machine learning"},{"key":"3299_CR32","doi-asserted-by":"crossref","unstructured":"Kurakin, A., Goodfellow, I., Bengio, S., Dong, Y., Liao, F., Liang, M., et al. (2018). Adversarial attacks and defences competition. In The NIPS'17 competition: Building intelligent systems (pp. 195\u2013231).","DOI":"10.1007\/978-3-319-94042-7_11"},{"key":"3299_CR33","unstructured":"Tram\u00e8r, F., Kurakin, A., Papernot, N., Goodfellow, I., Boneh, D., & McDaniel, P. (2017). Ensemble adversarial training: Attacks and defenses. arXiv preprint arXiv:1705.07204."},{"key":"3299_CR34","unstructured":"Kannan, H., Kurakin, A., & Goodfellow, I. (2018). Adversarial logit pairing. arXiv preprint arXiv:1803.06373."},{"key":"3299_CR35","unstructured":"Wang, Y., Zou, D., Yi, J., Bailey, J., Ma, X., & Gu, Q. (2019). Improving adversarial robustness requires revisiting misclassified examples. In International conference on learning representations."},{"key":"3299_CR36","doi-asserted-by":"crossref","unstructured":"Papernot, N., McDaniel, P., Wu, X., Jha, S., & Swami, A. (2016). Distillation as a defense to adversarial perturbations against deep neural networks. In IEEE symposium on security and privacy (pp. 582\u2013597).","DOI":"10.1109\/SP.2016.41"},{"key":"3299_CR37","doi-asserted-by":"crossref","unstructured":"Gao, Q., Cao, Z., & Li, D. (2021). Defensive distillation based end-to-end auto-encoder communication system. In 7th international conference on computer and communications (pp. 109\u2013114).","DOI":"10.1109\/ICCC54389.2021.9674255"},{"key":"3299_CR38","doi-asserted-by":"publisher","first-page":"98191","DOI":"10.1109\/ACCESS.2022.3206385","volume":"10","author":"FO Catak","year":"2022","unstructured":"Catak, F. O., Kuzlu, M., Catak, E., Cali, U., & Guler, O. (2022). Defensive distillation-based adversarial attack mitigation method for channel estimation using deep learning models in next-generation wireless networks. IEEE Access, 10, 98191\u201398203.","journal-title":"IEEE Access"},{"key":"3299_CR39","unstructured":"Cohen, J., Rosenfeld, E., & Kolter, Z. (2019). Certified adversarial robustness via randomized smoothing. In International conference on machine learning (pp. 1310\u20131320)."},{"key":"3299_CR40","first-page":"6465","volume":"33","author":"A Levine","year":"2020","unstructured":"Levine, A., & Feizi, S. (2020). (De)Randomized smoothing for certifiable defense against patch attacks. Neural Information Processing Systems, 33, 6465\u20136475.","journal-title":"Neural Information Processing Systems"},{"key":"3299_CR41","doi-asserted-by":"crossref","unstructured":"Jia, J., Cao, X., Wang, B., & Gong, N. Z. (2019). Certified robustness for top-k predictions against adversarial perturbations via randomized smoothing. In International conference on learning representations.","DOI":"10.1145\/3366423.3380029"},{"key":"3299_CR42","first-page":"2316","volume":"33","author":"D Zhang","year":"2020","unstructured":"Zhang, D., Ye, M., Gong, C., Zhu, Z., & Liu, Q. (2020). Black-box certification with randomized smoothing: A Functional Optimization based Framework. Neural Information Processing Systems, 33, 2316\u20132326.","journal-title":"Neural Information Processing Systems"},{"key":"3299_CR43","doi-asserted-by":"crossref","unstructured":"Maroto, J., Bovet, G., & Frossard, P. (2022). SafeAMC: Adversarial training for robust modulation classification recognition models. In 30th European signal processing conference (pp. 1636\u20131640).","DOI":"10.23919\/EUSIPCO55093.2022.9909941"}],"container-title":["Wireless Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11276-023-03299-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11276-023-03299-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11276-023-03299-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T12:13:58Z","timestamp":1695298438000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11276-023-03299-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,22]]},"references-count":43,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["3299"],"URL":"https:\/\/doi.org\/10.1007\/s11276-023-03299-4","relation":{},"ISSN":["1022-0038","1572-8196"],"issn-type":[{"value":"1022-0038","type":"print"},{"value":"1572-8196","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,22]]},"assertion":[{"value":"15 February 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 March 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}