{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T07:07:05Z","timestamp":1773990425979,"version":"3.50.1"},"reference-count":113,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,2,16]],"date-time":"2025-02-16T00:00:00Z","timestamp":1739664000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,16]],"date-time":"2025-02-16T00:00:00Z","timestamp":1739664000000},"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":["Int. J. Inf. Secur."],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1007\/s10207-025-00997-2","type":"journal-article","created":{"date-parts":[[2025,2,16]],"date-time":"2025-02-16T14:59:32Z","timestamp":1739717972000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["USB-GATE: USB-based GAN-augmented transformer reinforced defense framework for adversarial keystroke injection attacks"],"prefix":"10.1007","volume":"24","author":[{"given":"Anil Kumar","family":"Chillara","sequence":"first","affiliation":[]},{"given":"Paresh","family":"Saxena","sequence":"additional","affiliation":[]},{"given":"Rajib Ranjan","family":"Maiti","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,16]]},"reference":[{"key":"997_CR1","unstructured":"The Insight Partners: USB device market-size and share (2021 [Online]). https:\/\/www.theinsightpartners.com\/reports\/usb-device-market"},{"key":"997_CR2","unstructured":"Market Research Future: USB devices market (2022 [Online]). https:\/\/www.marketresearchfuture.com\/thank-you-sample?report_id=8671"},{"key":"997_CR3","unstructured":"SNS Insider: USB devices market report scope & overview (2022 [Online]). https:\/\/www.snsinsider.com\/reports\/usb-devices-market-2604"},{"key":"997_CR4","unstructured":"CORO. Why usb attacks are back and how to prevent them (2024 [Online]). https:\/\/www.coro.net\/blog\/why-usb-attacks-are-back-and-how-to-prevent-them"},{"key":"997_CR5","unstructured":"Dark Reading: The weirdest trend in cybersecurity\u2019: Nation-states returning to usbs (2024 [Online]). https:\/\/www.darkreading.com\/ics-ot-security\/weirdest-trend-cybersecurity-nation-states-usb"},{"issue":"2","key":"997_CR6","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1504\/IJSSE.2020.109127","volume":"10","author":"G Assenza","year":"2020","unstructured":"Assenza, G., Faramondi, L., Oliva, G., Setola, R.: Cyber threats for operational technologies. Int. J. Syst. Syst. Eng. 10(2), 128 (2020)","journal-title":"Int. J. Syst. Syst. Eng."},{"key":"997_CR7","unstructured":"Guri, M.: Mind The Gap: Can air-gaps keep your private data secure?. Preprint at arXiv:2409.04190 (2024)"},{"key":"997_CR8","unstructured":"Honeywell. Honeywell cybersecurity research reports significant increase in USB threats that can cause costly business disruptions (2021 [Online]). https:\/\/www.honeywell.com\/us\/en\/press\/2021\/06\/"},{"key":"997_CR9","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1016\/j.cose.2017.08.002","volume":"70","author":"N Nissim","year":"2017","unstructured":"Nissim, N., Yahalom, R., Elovici, Y.: USB-based attacks. Comput. Secur. 70, 675 (2017). https:\/\/doi.org\/10.1016\/j.cose.2017.08.002","journal-title":"Comput. Secur."},{"key":"997_CR10","doi-asserted-by":"publisher","unstructured":"Mamchenko, M., Sabanov, A.: Exploring the Taxonomy of USB-Based Attacks. In: 2019 Twelfth International Conference \"Management of large-scale system development\" (MLSD), pp. 1\u20134, (2019). https:\/\/doi.org\/10.1109\/MLSD.2019.8910969","DOI":"10.1109\/MLSD.2019.8910969"},{"key":"997_CR11","doi-asserted-by":"crossref","unstructured":"Lalithaditya, P., Somanadh, M.V., Rani, P., Rani, P., Sachan, R.K.: APMVU: Exploit Pentesting of user account settings with Arduino-based pro micro USB and microcontroller. In: 2024 First International Conference on Innovations in Communications, Electrical and Computer Engineering (ICICEC), IEEE, pp. 1\u20136 (2024)","DOI":"10.1109\/ICICEC62498.2024.10808439"},{"key":"997_CR12","first-page":"1230","volume":"3","author":"P Eswar","year":"2021","unstructured":"Eswar, P.: Microcontroller manipulated as human interface device performing keystroke injection attack. Int. Res. J. Mod. Eng. Technol. Sci 3, 1230 (2021)","journal-title":"Int. Res. J. Mod. Eng. Technol. Sci"},{"key":"997_CR13","doi-asserted-by":"publisher","DOI":"10.5220\/0011677100003405","author":"M Nicho","year":"2023","unstructured":"Nicho, M., Sabry, I.: Bypassing multiple security layers using malicious USB human interface device. SCITEPRESS-Sci. Technol. Publ. (2023). https:\/\/doi.org\/10.5220\/0011677100003405","journal-title":"SCITEPRESS-Sci. Technol. Publ."},{"key":"997_CR14","doi-asserted-by":"crossref","unstructured":"Mueller, T., Zimmer, E., de\u00a0Nittis, L.: Using context and provenance to defend against usb-borne attacks. In: Proceedings of the 14th international conference on availability, reliability and security, pp. 1\u20139 (2019)","DOI":"10.1145\/3339252.3339268"},{"key":"997_CR15","doi-asserted-by":"crossref","unstructured":"Tian, D.J., Bates, A., Butler, K.: Defending against malicious USB firmware with GoodUSB. In: Proceedings of the 31st Annual Computer Security Applications Conference, pp. 261\u2013270 (2015)","DOI":"10.1145\/2818000.2818040"},{"key":"997_CR16","doi-asserted-by":"crossref","unstructured":"Hernandez, G., Fowze, F., Tian, D., Yavuz, T., Butler, K.R.: Firmusb: Vetting usb device firmware using domain informed symbolic execution. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 2245\u20132262 (2017)","DOI":"10.1145\/3133956.3134050"},{"key":"997_CR17","unstructured":"Kharraz, A., Daley, B.L., Baker, G.Z., Robertson, W., Kirda, E.: $$\\{USBESAFE\\}$$: an $$\\{End-Point\\}$$ solution to protect against $$\\{USB-Based\\}$$ attacks. In: 22nd International Symposium on Research in Attacks, Intrusions and Defenses (RAID 2019), pp. 89\u2013103 (2019)"},{"key":"997_CR18","doi-asserted-by":"crossref","unstructured":"Lu, H., Wu, Y., Li, S., Lin, Y., Zhang, C., Zhang, F.: Badusb-c: Revisiting badusb with type-c. In: 2021 IEEE Security and Privacy Workshops (SPW) (IEEE, 2021), pp. 327\u2013338","DOI":"10.1109\/SPW53761.2021.00053"},{"key":"997_CR19","unstructured":"Tian, D.J., Scaife, N., Bates, A., Butler, K., Traynor, P.: Making $$\\{$$USB$$\\}$$ great again with $$\\{$$USBFILTER$$\\}$$. In: 25th USENIX Security Symposium (USENIX Security 16), pp. 415\u2013430 (2016)"},{"key":"997_CR20","doi-asserted-by":"publisher","unstructured":"Tian, J., Scaife, N., Kumar, D., Bailey, M., Bates, A., Butler, K.: SoK: \u201cPlug & Pray\u201d Today \u2013 Understanding USB Insecurity in Versions 1 Through C. In: 2018 IEEE Symposium on Security and Privacy (SP) (2018), pp. 1032\u20131047. https:\/\/doi.org\/10.1109\/SP.2018.00037","DOI":"10.1109\/SP.2018.00037"},{"key":"997_CR21","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Hsu, F.H.: USBIPS framework: protecting hosts from malicious USB peripherals. Preprint at arXiv:2409.12850 (2024)","DOI":"10.2139\/ssrn.4847047"},{"key":"997_CR22","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1007\/s41635-020-00092-z","volume":"4","author":"K Denney","year":"2020","unstructured":"Denney, K., Babun, L., Uluagac, A.S.: USB-watch: A generalized hardware-assisted insider threat detection framework. J. Hardware Syst. Secur. 4, 136 (2020)","journal-title":"J. Hardware Syst. Secur."},{"key":"997_CR23","doi-asserted-by":"crossref","unstructured":"Neuner, S., Voyiatzis, A.G., Fotopoulos, S., Mulliner, C., Weippl, E.R.: Usblock: blocking usb-based keypress injection attacks. In: Data and Applications Security and Privacy XXXII: 32nd Annual IFIP WG 11.3 Conference, DBSec 2018, Bergamo, Italy, July 16\u201318, 2018, Proceedings 32, Springer, pp. 278\u2013295, (2018)","DOI":"10.1007\/978-3-319-95729-6_18"},{"key":"997_CR24","doi-asserted-by":"crossref","unstructured":"Cronin, P., Gao, X., Wang, H., Cotton, C.: Time-print: Authenticating USB flash drives with novel timing fingerprints. In: 2022 IEEE Symposium on Security and Privacy (SP), IEEE, pp. 1002\u20131017 (2022)","DOI":"10.1109\/SP46214.2022.9833595"},{"issue":"1","key":"997_CR25","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1145\/3422308","volume":"20","author":"OA Ibrahim","year":"2020","unstructured":"Ibrahim, O.A., Sciancalepore, S., Oligeri, G., Pietro, R.D.: MAGNETO: fingerprinting USB flash drives via unintentional magnetic emissions. ACM Trans. Embed. Comput. Syst. 20(1), 26 (2020). https:\/\/doi.org\/10.1145\/3422308","journal-title":"ACM Trans. Embed. Comput. Syst."},{"key":"997_CR26","doi-asserted-by":"crossref","unstructured":"Ahmed, I.M., Kashmoola, M.Y.: Threats on machine learning technique by data poisoning attack: A survey. In: Advances in Cyber Security: Third International Conference, ACeS 2021, Penang, Malaysia, August 24\u201325, 2021, Revised Selected Papers 3, Springer, pp. 586\u2013600 (2021)","DOI":"10.1007\/978-981-16-8059-5_36"},{"issue":"13s","key":"997_CR27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3585385","volume":"55","author":"AE Cin\u00e0","year":"2023","unstructured":"Cin\u00e0, A.E., Grosse, K., Demontis, A., Vascon, S., Zellinger, W., Moser, B.A., Oprea, A., Biggio, B., Pelillo, M., Roli, F.: Wild patterns reloaded: a survey of machine learning security against training data poisoning. ACM Comput. Surv. 55(13s), 1 (2023)","journal-title":"ACM Comput. Surv."},{"key":"997_CR28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10207-024-00834-y","volume":"23","author":"AK Chillara","year":"2024","unstructured":"Chillara, A.K., Saxena, P., Maiti, R.R., Gupta, M., Kondapalli, R., Zhang, Z., Kesavan, K.: Deceiving supervised machine learning models via adversarial data poisoning attacks: a case study with USB keyboards. Int. J. Inf. Secur. 23, 1\u201319 (2024)","journal-title":"Int. J. Inf. Secur."},{"issue":"7","key":"997_CR29","first-page":"2578","volume":"31","author":"J Zhang","year":"2019","unstructured":"Zhang, J., Li, C.: Adversarial examples: opportunities and challenges. IEEE Trans. Neural Netw. Learn. Syst. 31(7), 2578 (2019)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"9","key":"997_CR30","doi-asserted-by":"crossref","first-page":"2805","DOI":"10.1109\/TNNLS.2018.2886017","volume":"30","author":"X Yuan","year":"2019","unstructured":"Yuan, X., He, P., Zhu, Q., Li, X.: Adversarial examples: attacks and defenses for deep learning. IEEE Trans. Neural Netw. Learn. Syst. 30(9), 2805 (2019)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"997_CR31","doi-asserted-by":"crossref","unstructured":"Mode, G.R., Hoque, K.A.: Adversarial examples in deep learning for multivariate time series regression. In: 2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), IEEE, pp. 1\u201310 (2020)","DOI":"10.1109\/AIPR50011.2020.9425190"},{"key":"997_CR32","doi-asserted-by":"crossref","unstructured":"Fan, J., Yan, Q., Li, M., Qu, G., Xiao, Y.: A survey on data poisoning attacks and defenses. In: 2022 7th IEEE International Conference on Data Science in Cyberspace (DSC), IEEE, pp. 48\u201355 (2022)","DOI":"10.1109\/DSC55868.2022.00014"},{"key":"997_CR33","unstructured":"Hack5. Hack5 rubber ducky device quack like a keyboard (Accessed Apr. 04, 2022 [Online]). https:\/\/shop.hak5.org\/products\/usb-rubber-ducky-deluxe"},{"issue":"2","key":"997_CR34","first-page":"6671","volume":"15","author":"B Cannoles","year":"2017","unstructured":"Cannoles, B., Ghafarian, A.: Hacking experiment by using usb rubber ducky scripting. J. Syst. 15(2), 6671 (2017)","journal-title":"J. Syst."},{"key":"997_CR35","doi-asserted-by":"crossref","unstructured":"Jothi, N.A., Anu, S., Harsha, K., Priya, R.D.: USB Rubber Ducky Hunter A Proactive Defense Against Malicious USB Attacks Domain: Cybersecurity. In: 2024 International Conference on Intelligent Systems for Cybersecurity (ISCS), IEEE, pp. 1\u20136 (2024)","DOI":"10.1109\/ISCS61804.2024.10581045"},{"key":"997_CR36","doi-asserted-by":"crossref","unstructured":"Ramadhanty, A.D., Budiono, A., Almaarif, A.: Implementation and analysis of keyboard injection attack using USB devices in windows operating system. In: 2020 3rd International Conference on Computer and Informatics Engineering (IC2IE) (2020)","DOI":"10.1109\/IC2IE50715.2020.9274631"},{"key":"997_CR37","doi-asserted-by":"crossref","unstructured":"Shafique, U., Zahur, S.B.: Towards protection against a usb device whose firmware has been compromised or turned as \u2018BadUSB\u2019. In: Advances in Information and Communication: Proceedings of the 2019 Future of Information and Communication Conference (FICC), Vol. 2, Springer, pp. 975\u2013987 (2020)","DOI":"10.1007\/978-3-030-12385-7_66"},{"key":"997_CR38","unstructured":"Qfx Software Corporation: Keyscrambler. https:\/\/www.qfxsoftware.com\/. Accessed July 2024. (2006)"},{"key":"997_CR39","doi-asserted-by":"crossref","unstructured":"Negi, A., Rathore, S.S., Sadhya, D.: USB keypress injection attack detection via free-text keystroke dynamics. In: 2021 8th International Conference on Signal Processing and Integrated Networks (SPIN), IEEE, pp. 681\u2013685 (2021)","DOI":"10.1109\/SPIN52536.2021.9566083"},{"key":"997_CR40","doi-asserted-by":"publisher","unstructured":"Erdin, E., Aksu, H., Uluagac, S., Vai, M., Akkaya, K.: OS Independent and Hardware-Assisted Insider Threat Detection and Prevention Framework. In: MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM), pp. 926\u2013932, (2018). https:\/\/doi.org\/10.1109\/MILCOM.2018.8599719","DOI":"10.1109\/MILCOM.2018.8599719"},{"key":"997_CR41","doi-asserted-by":"crossref","unstructured":"Aryal, K., Gupta, M., Abdelsalam, M.: Analysis of label-flip poisoning attack on machine learning based malware detector. In: 2022 IEEE International Conference on Big Data (Big Data), IEEE, pp. 4236\u20134245 (2022)","DOI":"10.1109\/BigData55660.2022.10020528"},{"issue":"2","key":"997_CR42","doi-asserted-by":"crossref","first-page":"1563","DOI":"10.1109\/TPAMI.2022.3162397","volume":"45","author":"M Goldblum","year":"2022","unstructured":"Goldblum, M., Tsipras, D., Xie, C., Chen, X., Schwarzschild, A., Song, A., Dawn, B., Li, T. Goldstein.: Dataset security for machine learning: data poisoning, backdoor attacks, and defenses. IEEE Trans. Pattern Anal. Mach. Intell. 45(2), 1563 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"997_CR43","unstructured":"Geiping, J., Fowl, L., Huang, W.R., Czaja, W., Taylor, G., Moeller, M., Goldstein, T.: Witches\u2019 brew: Industrial scale data poisoning via gradient matching. Preprint at arXiv:2009.02276 (2020)"},{"key":"997_CR44","unstructured":"Chang, X., Dost, K., Dobbie, G., Wicker, J.: Poison is Not Traceless: Fully-Agnostic Detection of Poisoning Attacks. Preprint at arXiv:2310.16224 (2023)"},{"key":"997_CR45","doi-asserted-by":"crossref","first-page":"3412","DOI":"10.1109\/TIFS.2021.3080522","volume":"16","author":"J Chen","year":"2021","unstructured":"Chen, J., Zhang, X., Zhang, R., Wang, C., Liu, L.: De-pois: an attack-agnostic defense against data poisoning attacks. IEEE Trans. Inf. Forensics Secur. 16, 3412 (2021)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"997_CR46","doi-asserted-by":"crossref","unstructured":"Park, L.H., Kim, J., Oh, M.G., Park, J., Kwon, T.: Adversarial feature alignment: Balancing robustness and accuracy in deep learning via adversarial training. In: Proceedings of the 2024 Workshop on Artificial Intelligence and Security, pp. 101\u2013112 (2024)","DOI":"10.1145\/3689932.3694765"},{"key":"997_CR47","unstructured":"USB-IF. USB4 language product and packaging guidelines final (Last accessed 2021 [Online]). https:\/\/www.usb.org\/sites\/default\/files\/usb4_language_product_and_packaging_guidelines_final__0.pdf"},{"key":"997_CR48","doi-asserted-by":"publisher","unstructured":"USB-IF. USB implementers forum. Accessed Apr. 04, 2022 [Online]. https:\/\/doi.org\/10.1109\/IEEESTD.2000.92296. https:\/\/www.usb.org\/about","DOI":"10.1109\/IEEESTD.2000.92296"},{"issue":"1","key":"997_CR49","first-page":"1","volume":"20","author":"OA Ibrahim","year":"2020","unstructured":"Ibrahim, O.A., Sciancalepore, S., Oligeri, G., Pietro, R.D.: MAGNETO: fingerprinting USB flash drives via unintentional magnetic emissions. ACM Trans. Embed. Comput. Syst. (TECS) 20(1), 1 (2020)","journal-title":"ACM Trans. Embed. Comput. Syst. (TECS)"},{"key":"997_CR50","unstructured":"Liu, T.Y., Yang, Y., Mirzasoleiman, B.: in Advances in Neural Information Processing Systems, vol.\u00a035, ed. by S.\u00a0Koyejo, S.\u00a0Mohamed, A.\u00a0Agarwal, D.\u00a0Belgrave, K.\u00a0Cho, A.\u00a0Oh (Curran Associates, Inc., 2022), vol.\u00a035, pp. 11,947\u201311,959. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2022\/file\/4e81308aa2eb8e2e4eccf122d4827af7-Paper-Conference.pdf"},{"key":"997_CR51","doi-asserted-by":"crossref","DOI":"10.1016\/j.cosrev.2019.100199","volume":"34","author":"N Pitropakis","year":"2019","unstructured":"Pitropakis, N., Panaousis, E., Giannetsos, T., Anastasiadis, E., Loukas, G.: A taxonomy and survey of attacks against machine learning. Comput. Sci. Rev. 34, 100199 (2019)","journal-title":"Comput. Sci. Rev."},{"key":"997_CR52","unstructured":"Wu, B., Wei, S., Zhu, M., Zheng, M., Zhu, Z., Zhang, M., Chen, H., Yuan, D., Liu, L., Liu, Q.: Defenses in adversarial machine learning: A survey. Preprint at arXiv:2312.08890 (2023)"},{"key":"997_CR53","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s11633-019-1211-x","volume":"17","author":"H Xu","year":"2020","unstructured":"Xu, H., Ma, Y., Liu, H.C., Deb, D., Liu, H., Tang, J.L., Jain, A.K.: Adversarial attacks and defenses in images, graphs and text: a review. Int. J. Autom. Comput. 17, 151 (2020)","journal-title":"Int. J. Autom. Comput."},{"issue":"1","key":"997_CR54","first-page":"20240153","volume":"33","author":"YL Khaleel","year":"2024","unstructured":"Khaleel, Y.L., Habeeb, M.A., Albahri, A., Al-Quraishi, T., Albahri, O., Alamoodi, A.: Network and cybersecurity applications of defense in adversarial attacks: a state-of-the-art using machine learning and deep learning methods. J. Intell Syst. 33(1), 20240153 (2024)","journal-title":"J. Intell Syst."},{"key":"997_CR55","doi-asserted-by":"publisher","unstructured":"Zantedeschi, V., Nicolae, M.I., Rawat, A.: Efficient Defenses Against Adversarial Attacks. In: Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security (Association for Computing Machinery, New York, NY, USA, 2017), AISec \u201917, p. 39-49. https:\/\/doi.org\/10.1145\/3128572.3140449","DOI":"10.1145\/3128572.3140449"},{"issue":"5","key":"997_CR56","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3453158","volume":"54","author":"I Rosenberg","year":"2021","unstructured":"Rosenberg, I., Shabtai, A., Elovici, Y., Rokach, L.: Adversarial machine learning attacks and defense methods in the cyber security domain. ACM Comput. Surv. (CSUR) 54(5), 1 (2021)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"997_CR57","doi-asserted-by":"crossref","DOI":"10.1016\/j.cosrev.2023.100573","volume":"49","author":"P Bountakas","year":"2023","unstructured":"Bountakas, P., Zarras, A., Lekidis, A., Xenakis, C.: Defense strategies for adversarial machine learning: a survey. Comput. Sci. Rev. 49, 100573 (2023)","journal-title":"Comput. Sci. Rev."},{"key":"997_CR58","doi-asserted-by":"crossref","unstructured":"Wang, Y., Sun, T., Li, S., Yuan, X., Ni, W., Hossain, E., Poor, H.V.: Adversarial attacks and defenses in machine learning-empowered communication systems and networks: A contemporary survey. IEEE Commun. Surv. Tutor. (2023)","DOI":"10.1109\/COMST.2023.3319492"},{"key":"997_CR59","doi-asserted-by":"crossref","unstructured":"Vassilev, A., Oprea, A., Fordyce, A., Anderson, H.: Adversarial machine learning: a taxonomy and terminology of attacks and mitigations. Tech. rep, National Institute of Standards and Technology (2024)","DOI":"10.6028\/NIST.AI.100-2e2023"},{"key":"997_CR60","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. Advances in neural information processing systems 27 (2014)"},{"issue":"11","key":"997_CR61","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. Commun. ACM 63(11), 139 (2020)","journal-title":"Commun. ACM"},{"key":"997_CR62","unstructured":"Semi-supervised Learning: Semi-supervised learning. CSZ2006. html 5, 2 (2006)"},{"issue":"9","key":"997_CR63","doi-asserted-by":"crossref","first-page":"8934","DOI":"10.1109\/TKDE.2022.3220219","volume":"35","author":"X Yang","year":"2022","unstructured":"Yang, X., Song, Z., King, I., Xu, Z.: A survey on deep semi-supervised learning. IEEE Trans. Knowl. Data Eng. 35(9), 8934 (2022)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"1","key":"997_CR64","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1109\/MSP.2017.2765202","volume":"35","author":"A Creswell","year":"2018","unstructured":"Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., Bharath, A.A.: Generative adversarial networks: an overview. IEEE Signal Process. Magaz. 35(1), 53 (2018). https:\/\/doi.org\/10.1109\/MSP.2017.2765202","journal-title":"IEEE Signal Process. Magaz."},{"issue":"15","key":"997_CR65","doi-asserted-by":"crossref","first-page":"2733","DOI":"10.3390\/math10152733","volume":"10","author":"A Figueira","year":"2022","unstructured":"Figueira, A., Vaz, B.: Survey on synthetic data generation, evaluation methods and GANs. Mathematics 10(15), 2733 (2022)","journal-title":"Mathematics"},{"key":"997_CR66","doi-asserted-by":"crossref","unstructured":"Usama, M., Asim, M., Latif, S., Qadir, J., et\u00a0al.: Generative adversarial networks for launching and thwarting adversarial attacks on network intrusion detection systems. In: 2019 15th international wireless communications & mobile computing conference (IWCMC), IEEE, pp. 78\u201383 (2019)","DOI":"10.1109\/IWCMC.2019.8766353"},{"key":"997_CR67","unstructured":"Chen, M., Liao, W., Zha, H., Zhao, T.: Distribution approximation and statistical estimation guarantees of generative adversarial networks. Preprint at arXiv:2002.03938 (2020)"},{"key":"997_CR68","unstructured":"Radford, A., Metz, L., Chintala, S.: Distribution approximation and statistical estimation guarantees of generative adversarial networks. Preprint at arXiv:1511.06434 (2015)"},{"issue":"2","key":"997_CR69","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s10462-023-10624-y","volume":"57","author":"MM Saad","year":"2024","unstructured":"Saad, M.M., O\u2019Reilly, R., Rehmani, M.H.: A survey on training challenges in generative adversarial networks for biomedical image analysis. Artif. Intell. Rev. 57(2), 19 (2024)","journal-title":"Artif. Intell. Rev."},{"key":"997_CR70","unstructured":"Arjovsky, M., Bottou, L.: Towards principled methods for training generative adversarial networks. Preprint at arXiv:1701.04862 (2017)"},{"key":"997_CR71","unstructured":"Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training gans. Advances in neural information processing systems 29 (2016)"},{"key":"997_CR72","doi-asserted-by":"crossref","unstructured":"Z.\u00a0Zhang, M.\u00a0Li, J.\u00a0Yu, in SIGGRAPH Asia 2018 Technical Briefs (2018), pp. 1\u20134","DOI":"10.1145\/3283254.3283282"},{"key":"997_CR73","unstructured":"Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: International conference on machine learning, PMLR, pp. 214\u2013223 (2017)"},{"issue":"8","key":"997_CR74","doi-asserted-by":"publisher","first-page":"1720","DOI":"10.1109\/TNSRE.2020.3006180","volume":"28","author":"S Panwar","year":"2020","unstructured":"Panwar, S., Rad, P., Jung, T.P., Huang, Y.: Modeling EEG data distribution with a Wasserstein generative adversarial network to predict RSVP events. IEEE Trans. Neural Syst. Rehabil. Eng. 28(8), 1720 (2020). https:\/\/doi.org\/10.1109\/TNSRE.2020.3006180","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"997_CR75","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of wasserstein gans. Advances in neural information processing systems 30 (2017)"},{"key":"997_CR76","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.image.2019.03.010","volume":"75","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y., Sun, B., Xiao, Y., Xiao, R., Wei, Y.: Feature augmentation for imbalanced classification with conditional mixture WGANs. Signal Process. Image Commun. 75, 89 (2019)","journal-title":"Signal Process. Image Commun."},{"key":"997_CR77","unstructured":"Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for GANs do actually converge?. In: International conference on machine learning, PMLR, pp. 3481\u20133490 (2018)"},{"key":"997_CR78","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of stylegan. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 8110\u20138119 (2020)","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"997_CR79","doi-asserted-by":"crossref","unstructured":"Kwon, Y.H., Park, M.G.: Predicting future frames using retrospective cycle gan. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1811\u20131820 (2019)","DOI":"10.1109\/CVPR.2019.00191"},{"key":"997_CR80","doi-asserted-by":"crossref","unstructured":"Li, Z., Ma, C., Shi, X., Zhang, D., Li, W., Wu, L.: Tsa-gan: A robust generative adversarial networks for time series augmentation. In: 2021 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1\u20138 (2021)","DOI":"10.1109\/IJCNN52387.2021.9534001"},{"key":"997_CR81","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention Is All You Need.(Nips), 2017. Preprint at arXiv:1706.0376210, S0140525X16001837 (2017)"},{"key":"997_CR82","volume":"253","author":"S Zuo","year":"2022","unstructured":"Zuo, S., Xiao, Y., Chang, X., Wang, X.: Vision transformers for dense prediction: a survey. Knowl.-Based Syst. 253, 109552 (2022)","journal-title":"Knowl.-Based Syst."},{"key":"997_CR83","doi-asserted-by":"crossref","unstructured":"Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. Preprint at arXiv:2202.07125 (2022)","DOI":"10.24963\/ijcai.2023\/759"},{"issue":"1","key":"997_CR84","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.dcan.2023.03.008","volume":"10","author":"F Ullah","year":"2024","unstructured":"Ullah, F., Ullah, S., Srivastava, G., Lin, J.C.W.: IDS-INT: intrusion detection system using transformer-based transfer learning for imbalanced network traffic. Digit. Commun. Netw. 10(1), 190 (2024)","journal-title":"Digit. Commun. Netw."},{"key":"997_CR85","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., Sutskever, I.: Learning transferable visual models from natural language supervision. CoRR abs\/2103.00020 (2021). arXiv: 2103.00020"},{"key":"997_CR86","unstructured":"Wang, L., Xiang, L., Wei, Y., Wang, Y., He, Z.: CLIP model is an efficient online lifelong learner. Preprint at arXiv:2405.15155 (2024)"},{"key":"997_CR87","doi-asserted-by":"crossref","unstructured":"Xian, Y., Akata, Z., Sharma, G., Nguyen, Q., Hein, M., Schiele, B.: Latent embeddings for zero-shot classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 69\u201377 (2016)","DOI":"10.1109\/CVPR.2016.15"},{"key":"997_CR88","unstructured":"An, B., Zhu, S., Panaitescu-Liess, M.A., Mummadi, C.K., Huang, F.: PerceptionCLIP: visual classification by inferring and conditioning on contexts. In: The Twelfth International Conference on Learning Representations"},{"key":"997_CR89","doi-asserted-by":"crossref","unstructured":"Srivatsan, K., Naseer, M., Nandakumar, K.: Flip: Cross-domain face anti-spoofing with language guidance. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 19,685\u201319,696 (2023)","DOI":"10.1109\/ICCV51070.2023.01803"},{"key":"997_CR90","doi-asserted-by":"crossref","unstructured":"Zara, G., Roy, S., Rota, P., Ricci, E.: AutoLabel: CLIP-based framework for open-set video domain adaptation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2023), pp. 11,504\u201311,513","DOI":"10.1109\/CVPR52729.2023.01107"},{"key":"997_CR91","doi-asserted-by":"crossref","unstructured":"Babaria, R., Madanapalli, S.C., Kumar, H., Sivaraman, V.: Flowformers: Transformer-based models for real-time network flow classification. In: 2021 17th International Conference on Mobility, Sensing and Networking (MSN), IEEE, pp. 231\u2013238 (2021)","DOI":"10.1109\/MSN53354.2021.00046"},{"key":"997_CR92","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et\u00a0al.: An image is worth 16x16 words: Transformers for image recognition at scale. Preprint at arXiv:2010.11929 (2020)"},{"key":"997_CR93","doi-asserted-by":"crossref","unstructured":"Abdelfattah, R., Guo, Q., Li, X., Wang, X., Wang, S.: CDUL: CLIP-driven unsupervised learning for multi-label image classification. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 1348\u20131357 (2023)","DOI":"10.1109\/ICCV51070.2023.00130"},{"key":"997_CR94","doi-asserted-by":"crossref","unstructured":"Wu, P., Zhou, X., Pang, G., Zhou, L., Yan, Q., Wang, P., Zhang, Y.: Vadclip: Adapting vision-language models for weakly supervised video anomaly detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a038 , pp. 6074\u20136082 (2024)","DOI":"10.1609\/aaai.v38i6.28423"},{"key":"997_CR95","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et\u00a0al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning, PMLR, pp. 8748\u20138763 (2021)"},{"key":"997_CR96","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.cose.2017.10.008","volume":"73","author":"JM Hatfield","year":"2018","unstructured":"Hatfield, J.M.: Social engineering in cybersecurity: the evolution of a concept. Comput. Secur. 73, 102 (2018)","journal-title":"Comput. Secur."},{"key":"997_CR97","unstructured":"Samangouei, P., Kabkab, M., Chellappa, R.: Defense-gan: Protecting classifiers against adversarial attacks using generative models. Preprint at arXiv:1805.06605 (2018)"},{"key":"997_CR98","doi-asserted-by":"crossref","unstructured":"Zhang, K.: On mode collapse in generative adversarial networks. In: Artificial Neural Networks and Machine Learning\u2013ICANN 2021: 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14\u201317, 2021, Proceedings, Part II 30, Springer, pp. 563\u2013574 (2021)","DOI":"10.1007\/978-3-030-86340-1_45"},{"key":"997_CR99","doi-asserted-by":"crossref","unstructured":"Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., Davison, J., Shleifer, S., von Platen, P., Ma, C., Jernite, Y., Plu, J., Xu, C., Le\u00a0Scao, T., Gugger, S., Drame, M., Lhoest, Q., Rush, A.:Transformers: state-of-the-art natural language processing. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations (Association for Computational Linguistics, Online, 2020), pp. 38\u201345. https:\/\/www.aclweb.org\/anthology\/2020.emnlp-demos.6","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"issue":"21","key":"997_CR100","doi-asserted-by":"crossref","first-page":"3711","DOI":"10.1093\/bioinformatics\/bty373","volume":"34","author":"S Nembrini","year":"2018","unstructured":"Nembrini, S., K\u00f6nig, I.R., Wright, M.N.: The revival of the Gini importance? Bioinformatics 34(21), 3711 (2018)","journal-title":"Bioinformatics"},{"key":"997_CR101","unstructured":"Digistump. Digispark attiny85 (2015). http:\/\/digistump.com\/products\/1 [Accessed: (April 2023)]"},{"key":"997_CR102","unstructured":"Nvidia t4 gpu overview. https:\/\/www.nvidia.com\/en-us\/data-center\/tesla-t4\/. Accessed 27 July 2024"},{"key":"997_CR103","unstructured":"Nvidia data center products. https:\/\/www.nvidia.com\/en-us\/data-center\/. Accessed 27 July 2024"},{"key":"997_CR104","doi-asserted-by":"crossref","unstructured":"Yadav, P., Gaur, M., Fatima, N., Sarwar, S.: Qualitative and quantitative evaluation of multivariate time-series synthetic data generated using mts-tgan: a novel approach. Appl. Sci. 13(7), 4136 (2023)","DOI":"10.3390\/app13074136"},{"key":"997_CR105","doi-asserted-by":"publisher","unstructured":"Chillara AK, Saxena P, Maiti RR (2025) Transformer-based GAN-augmented Defender for Adversarial USB Keystroke Injection Attacks. In: Proceedings of the 26th International Conference on Distributed Computing and Networking, New York, NY, USA, pp. 94\u2013103. https:\/\/doi.org\/10.1145\/3700838.3700871","DOI":"10.1145\/3700838.3700871"},{"key":"997_CR106","unstructured":"Esteban, C., Hyland, S.L., R\u00e4tsch, G.: Real-valued (medical) time series generation with recurrent conditional gans. Preprint at arXiv:1706.02633 (2017)"},{"key":"997_CR107","unstructured":"Bergstra, J., Bardenet, R., Bengio, Y., K\u00e9gl, B.: Algorithms for hyper-parameter optimization. Advances in neural information processing systems 24 (2011)"},{"key":"997_CR108","unstructured":"Watanabe, S.: Tree-structured parzen estimator: Understanding its algorithm components and their roles for better empirical performance. Preprint at arXiv:2304.11127 (2023)"},{"issue":"2065","key":"997_CR109","first-page":"20150202","volume":"374","author":"IT Jolliffe","year":"2016","unstructured":"Jolliffe, I.T., Cadima, J.: Principal component analysis: a review and recent developments. Philos. Trans. R. Soc. Math. Phys. Eng. Sci. 374(2065), 20150202 (2016)","journal-title":"Philos. Trans. R. Soc. Math. Phys. Eng. Sci."},{"issue":"1","key":"997_CR110","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1038\/s43586-022-00184-w","volume":"2","author":"M Greenacre","year":"2022","unstructured":"Greenacre, M., Groenen, P.J., Hastie, T., d\u2019Enza, A.I., Markos, A., Tuzhilina, E.: Principal component analysis. Nat. Rev. Methods Prim. 2(1), 100 (2022)","journal-title":"Nat. Rev. Methods Prim."},{"issue":"1","key":"997_CR111","first-page":"20","volume":"2","author":"BMS Hasan","year":"2021","unstructured":"Hasan, B.M.S., Abdulazeez, A.M.: A review of principal component analysis algorithm for dimensionality reduction. J. Soft Comput. Data Min. 2(1), 20 (2021)","journal-title":"J. Soft Comput. Data Min."},{"key":"997_CR112","doi-asserted-by":"crossref","unstructured":"Reynolds, D.A., et\u00a0al: Gaussian mixture models. Encyclopedia of biometrics 741(659-663) (2009)","DOI":"10.1007\/978-0-387-73003-5_196"},{"key":"997_CR113","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.procs.2020.04.017","volume":"171","author":"E Patel","year":"2020","unstructured":"Patel, E., Kushwaha, D.S.: Clustering cloud workloads: K-means vs gaussian mixture model. Procedia Comput. Sci. 171, 158 (2020)","journal-title":"Procedia Comput. Sci."}],"container-title":["International Journal of Information Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10207-025-00997-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10207-025-00997-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10207-025-00997-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,30]],"date-time":"2025-03-30T08:01:59Z","timestamp":1743321719000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10207-025-00997-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,16]]},"references-count":113,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["997"],"URL":"https:\/\/doi.org\/10.1007\/s10207-025-00997-2","relation":{},"ISSN":["1615-5262","1615-5270"],"issn-type":[{"value":"1615-5262","type":"print"},{"value":"1615-5270","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,16]]},"assertion":[{"value":"16 February 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"79"}}