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In this article, we propose adopting a cutting-edge approach called Combinatorial Fusion Analysis (CFA), which leverages a recently developed framework to combine multiple ML models for improved DoS attack detection. Our methodology involves advanced score combination, rank combination, weighted combination techniques, and the diversity strength of scoring systems. Through rigorous performance evaluations, we showcase the efficacy of the combinatorial fusion approach. Our evaluations encompass key metrics such as detection precision, recall, and F1-score, providing comprehensive insights into the interpretability and effectiveness of our approach. We highlight the challenge faced by individual models in classifying low-profiled attacks, while excelling in other attack types. To overcome this limitation, model fusion techniques were used to create a comprehensive model capable of addressing both low-profiled attacks and other traffic types. Furthermore, our findings highlight the potential of this approach for enhancing DoS attack detection capabilities and contributing to the development of more robust defense mechanisms.<\/jats:p>","DOI":"10.1145\/3749374","type":"journal-article","created":{"date-parts":[[2025,7,18]],"date-time":"2025-07-18T11:32:41Z","timestamp":1752838361000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Generalizable Multi-Model Fusion for Multi-Class DoS Detection Using Cognitive Diversity and Rank-Score Analysis"],"prefix":"10.1145","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2495-264X","authenticated-orcid":false,"given":"Evans","family":"Owusu","sequence":"first","affiliation":[{"name":"Computer and Information Science, Fordham University","place":["New York, United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9701-5505","authenticated-orcid":false,"given":"Mohamed","family":"Rahouti","sequence":"additional","affiliation":[{"name":"Computer and Information Science, Fordham University","place":["New York, United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1933-7343","authenticated-orcid":false,"given":"Dinesh","family":"Verma","sequence":"additional","affiliation":[{"name":"IBM TJ Watson Research Center","place":["Yorktown Heights, United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1648-3575","authenticated-orcid":false,"given":"Yufeng","family":"Xin","sequence":"additional","affiliation":[{"name":"RENCI, The University of North Carolina at Chapel Hill","place":["Chapel Hill, United States"]}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9132-4367","authenticated-orcid":false,"given":"D. Frank","family":"Hsu","sequence":"additional","affiliation":[{"name":"Computer and Information Science, Fordham University","place":["New York, United States"]}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4721-8845","authenticated-orcid":false,"given":"Christina","family":"Schweikert","sequence":"additional","affiliation":[{"name":"CSMS Division, St John's University","place":["Queens, United States"]}]}],"member":"320","published-online":{"date-parts":[[2025,8,23]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","first-page":"102748","DOI":"10.1016\/j.cose.2022.102748","article-title":"A new DDoS attacks intrusion detection model based on deep learning for cybersecurity","volume":"118","author":"Akgun Devrim","year":"2022","unstructured":"Devrim Akgun, Selman Hizal, and Unal Cavusoglu. 2022. A new DDoS attacks intrusion detection model based on deep learning for cybersecurity. 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US Patent App. 17\/069 844."},{"key":"e_1_3_1_50_2","first-page":"25","volume-title":"Proceedings of the ICISSP","author":"Rosay Arnaud","year":"2022","unstructured":"Arnaud Rosay, Elo\u00efse Cheval, Florent Carlier, and Pascal Leroux. 2022. Network intrusion detection: A comprehensive analysis of CIC-IDS2017. In Proceedings of the ICISSP. SCITEPRESS-Science and Technology Publications, 25\u201336."},{"key":"e_1_3_1_51_2","first-page":"425","volume-title":"Proceedings of the CCNC","author":"Saha Sajal","year":"2022","unstructured":"Sajal Saha, Annita Tahsin Priyoti, Aakriti Sharma, and Anwar Haque. 2022. Towards an optimal feature selection method for AI-based DDoS detection system. In Proceedings of the CCNC. IEEE, 425\u2013428."},{"key":"e_1_3_1_52_2","article-title":"Deep ensemble learning with pruning for DDoS attack detection in IoT networks","author":"Saiyed Makhduma F.","year":"2024","unstructured":"Makhduma F. Saiyed and Irfan Al-Anbagi. 2024. 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Retrieved from https:\/\/arxiv.org\/abs\/2402.03646"},{"issue":"5","key":"e_1_3_1_63_2","doi-asserted-by":"crossref","first-page":"1422","DOI":"10.1021\/ci010025x","article-title":"How does consensus scoring work for virtual library screening? An idealized computer experiment","volume":"41","author":"Wang Renxiao","year":"2001","unstructured":"Renxiao Wang and Shaomeng Wang. 2001. How does consensus scoring work for virtual library screening? An idealized computer experiment. Journal of Chemical Information and Computer Sciences 41, 5 (2001), 1422\u20131426.","journal-title":"Journal of Chemical Information and Computer Sciences"},{"key":"e_1_3_1_64_2","doi-asserted-by":"publisher","DOI":"10.1145\/3663408.3663424"},{"key":"e_1_3_1_65_2","doi-asserted-by":"crossref","unstructured":"Nadia Niknami and Jie Wu. 2022. Entropy-KL-ML: Enhancing the entropy-KL-based anomaly detection on software-defined networks. 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