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However, they often lack the ability to collectively model both local and global traffic features, which presents challenges in improving performance. In order to provide an effective method for detecting abnormal traffic, this paper proposes a novel network architecture called DDoS\u2010MSCT, which combines a multiscale convolutional neural network and transformer. The DDoS\u2010MSCT architecture introduces the DDoS\u2010MSCT block, which consists of a local feature extraction module (LFEM) and a global feature extraction module (GFEM). The LFEM employs convolutional kernels of different sizes, accompanied by dilated convolutions, with the aim of enhancing the receptive field and capturing multiscale features simultaneously. On the other hand, the GFEM is utilized to capture long\u2010range dependencies for attending to global features. Furthermore, with the increase in network depth, DDoS\u2010MSCT facilitates the integration of multiscale local and global contextual information of traffic features, thereby improving detection performance. Our experiments are conducted on the CIC\u2010DDoS2019 dataset, and also the CIC\u2010IDS2017 dataset, which is introduced as a supplement to address the issue of sample imbalance. Experimental results on the hybrid dataset show that DDoS\u2010MSCT achieves accuracy, recall, F1 score, and precision of 99.94%, 99.95%, 99.95%, and 99.97%, respectively. Compared to the state of the art methods, the DDoS\u2010MSCT model achieves a good performance for detecting the DDoS attack to provide the protecting ability for network security.<\/jats:p>","DOI":"10.1049\/2024\/1056705","type":"journal-article","created":{"date-parts":[[2024,9,17]],"date-time":"2024-09-17T15:18:25Z","timestamp":1726586305000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["DDoS\u2010MSCT: A DDoS Attack Detection Method Based on Multiscale Convolution and Transformer"],"prefix":"10.1049","volume":"2024","author":[{"given":"Bangli","family":"Wang","sequence":"first","affiliation":[]},{"given":"Yuxuan","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"You","family":"Liao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3344-3653","authenticated-orcid":false,"given":"Zhen","family":"Li","sequence":"additional","affiliation":[]}],"member":"265","published-online":{"date-parts":[[2024,9,17]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2021.12.007"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2022.3187531"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-020-09942-2"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijinfomgt.2018.08.006"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2021.102580"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3090909"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1002\/int.23048"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2020.100279"},{"key":"e_1_2_10_9_2","doi-asserted-by":"crossref","unstructured":"SudarK. 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