{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T11:27:25Z","timestamp":1778153245671,"version":"3.51.4"},"reference-count":29,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T00:00:00Z","timestamp":1727913600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Institutional Fund Project","award":["1478-865-1443"],"award-info":[{"award-number":["1478-865-1443"]}]},{"name":"Ministry of Education and King Abdulaziz University, DSR, Jeddah, Saudi Arabia, Jeddah.","award":["1478-865-1443"],"award-info":[{"award-number":["1478-865-1443"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JSAN"],"abstract":"<jats:p>The increased use of Internet of Things (IoT) devices has led to greater threats to privacy and security. This has created a need for more effective cybersecurity applications. However, the effectiveness of these systems is often limited by the lack of comprehensive and balanced datasets. This research contributes to IoT security by tackling the challenges in dataset generation and providing a valuable resource for IoT security research. Our method involves creating a testbed, building the \u2018Joint Dataset\u2019, and developing an innovative tool. The tool consists of two modules: an Exploratory Data Analysis (EDA) module, and a Generator module. The Generator module uses a Conditional Generative Adversarial Network (CGAN) to address data imbalance and generate high-quality synthetic data that accurately represent real-world network traffic. To showcase the effectiveness of the tool, the proportion of imbalance reduction in the generated dataset was computed and benchmarked to the BOT-IOT dataset. The results demonstrated the robustness of synthetic data generation in creating balanced datasets.<\/jats:p>","DOI":"10.3390\/jsan13050062","type":"journal-article","created":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T06:45:52Z","timestamp":1727937952000},"page":"62","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Create a Realistic IoT Dataset Using Conditional Generative Adversarial Network"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4071-277X","authenticated-orcid":false,"given":"Miada","family":"Almasre","sequence":"first","affiliation":[{"name":"Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8642-1708","authenticated-orcid":false,"given":"Alanoud","family":"Subahi","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103054","DOI":"10.1016\/j.cose.2022.103054","article-title":"Synthetic attack data generation model applying generative adversarial network for intrusion detection","volume":"125","author":"Kumar","year":"2023","journal-title":"Comput. 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