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In this paper, we propose a double-layer feature extraction and feature fusion technique (CNN-GRU-FF), which uses a modified focal loss function instead of the traditional cross-entropy to handle the class imbalance problem in the IDS datasets. We use the NSL-KDD and UNSW-NB15 datasets to evaluate the effectiveness of the proposed model. From the research findings, it is evident our CNN-GRU-FF method obtains a detection rate of 98.22% and 99.68% using the UNSW-NB15 and NSL-KDD datasets, respectively while maintaining low false alarm rates on both datasets. We compared the proposed model\u2019s performance with seven baseline algorithms and other published methods in literature. It is evident from the performance results that our proposed method outperforms the state-of-the-art network intrusion detection methods.<\/jats:p>","DOI":"10.1007\/s40747-023-01313-y","type":"journal-article","created":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T03:02:58Z","timestamp":1706842978000},"page":"3353-3370","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["CNN-GRU-FF: a double-layer feature fusion-based network intrusion detection system using convolutional neural network and gated recurrent units"],"prefix":"10.1007","volume":"10","author":[{"given":"Yakubu","family":"Imrana","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9477-284X","authenticated-orcid":false,"given":"Yanping","family":"Xiang","sequence":"additional","affiliation":[]},{"given":"Liaqat","family":"Ali","sequence":"additional","affiliation":[]},{"given":"Adeeb","family":"Noor","sequence":"additional","affiliation":[]},{"given":"Kwabena","family":"Sarpong","sequence":"additional","affiliation":[]},{"given":"Muhammed Amin","family":"Abdullah","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,2]]},"reference":[{"key":"1313_CR1","unstructured":"Agarwal RK, Joshiy MV (2004) PNrule: a new framework for learning classifier models in data mining (a cast-study in network intrusion detection ) technical report"},{"key":"1313_CR2","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.knosys.2017.09.014","volume":"136","author":"H Wang","year":"2017","unstructured":"Wang H, Gu J, Wang S (2017) An effective intrusion detection framework based on SVM with feature augmentation. 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