{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T16:16:54Z","timestamp":1774541814726,"version":"3.50.1"},"reference-count":22,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T00:00:00Z","timestamp":1736380800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Internet Technology Letters"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>The proliferation of sophisticated malware poses an escalating threat to economic and national security, demanding a reevaluation of cybersecurity in next\u2010generation wireless networks like 6G. Integrating artificial intelligence (AI) offers a crucial opportunity to enhance network defenses against increasingly complex cyber threats. However, the growing complexity of 6G technologies exposes current Intrusion Detection Systems (IDS) to covert malware exploitation, underscoring the need for more adaptive and resilient detection mechanisms. In response to these challenges, this study presents MasqueradeGAN\u2010GP, an innovative framework based on Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN\u2010GP). The MasqueradeGAN\u2010GP framework consists of a generator, responsible for transforming raw malicious traffic into a semblance of benign activity, and a discriminator, which dynamically assimilates the feature set of the IDS to discern between genuine and adversarial traffic. Additionally, it includes a restrictive modification mechanism to ensure the fidelity of attack vectors. Experiments conducted on the CICIDS 2017 and NSL\u2010KDD datasets indicate that MasqueradeGAN\u2010GP effectively evades detection, suggesting its potential for advancing IDS capabilities and reinforcing AI\u2010driven security solutions within the 6G landscape. This contributes to building a more robust detection system capable of facing adversarial malware attacks in future wireless communications.<\/jats:p>","DOI":"10.1002\/itl2.640","type":"journal-article","created":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T23:50:10Z","timestamp":1736466610000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["<scp>MasqueradeGAN<\/scp>\n                    \u2010\n                    <scp>GP<\/scp>\n                    : A Generative Adversarial Network Framework for Evading Black\u2010Box Intrusion Detection Systems"],"prefix":"10.1002","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8932-4825","authenticated-orcid":false,"given":"Baishun","family":"Dong","sequence":"first","affiliation":[{"name":"China Academy of Launch Vehicle Technology Beijing Institute of Astronautical Systems Engineering  Beijing China"}]},{"given":"Hongsen","family":"Wang","sequence":"additional","affiliation":[{"name":"China Academy of Launch Vehicle Technology Beijing Institute of Astronautical Systems Engineering  Beijing China"}]},{"given":"Rubin","family":"Luo","sequence":"additional","affiliation":[{"name":"China Academy of Launch Vehicle Technology Beijing Institute of Astronautical Systems Engineering  Beijing China"}]}],"member":"311","published-online":{"date-parts":[[2025,1,9]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2009.05.029"},{"key":"e_1_2_8_3_1","article-title":"Zebra\n                  : Deeply Integrating System\u2010Level Provenance Search and Tracking for Efficient Attack Investigation","author":"Yang X.","year":"2022","journal-title":"arXiv"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2020.03.004"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2023.103300"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2019.02.064"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2023.03.024"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2022.09.026"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2021.3082330"},{"key":"e_1_2_8_10_1","article-title":"MalFox: Camouflaged Adversarial Malware Example Generation Based on Conv\u2010GANs Against Black\u2010Box Detectors","volume":"121","author":"Zhong F.","year":"2023","journal-title":"IEEE Transactions on Computers"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aej.2024.04.024"},{"key":"e_1_2_8_12_1","doi-asserted-by":"crossref","unstructured":"C.Li H.Liu Y.Liu et al. \u201cEndora: Video Generation Models as Endoscopy Simulators \u201d in International Conference on Medical Image Computing and Computer\u2010Assisted Intervention(Cham Switzerland: Springer Nature Switzerland 2024) 230\u2013240.","DOI":"10.1007\/978-3-031-72089-5_22"},{"key":"e_1_2_8_13_1","article-title":"Gaussianstego: A Generalizable Stenography Pipeline for Generative 3D Gaussians Splatting","author":"Li C.","year":"2024","journal-title":"arXiv"},{"key":"e_1_2_8_14_1","doi-asserted-by":"crossref","unstructured":"Y.Zhang C.Li X.Lin et al. \u201cGenerator Versus Segmentor: Pseudo\u2010Healthy Synthesis \u201d in Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference Strasbourg France Proceedings Part VI 24(Cham Switzerland: Springer International Publishing 2021):150\u2013160.","DOI":"10.1007\/978-3-030-87231-1_15"},{"key":"e_1_2_8_15_1","doi-asserted-by":"crossref","unstructured":"C.Li B. 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