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Priv. Secur."],"published-print":{"date-parts":[[2024,11,30]]},"abstract":"<jats:p>With the rapid advancements in internet technology, the complexity and sophistication of network traffic attacks are increasing, making it challenging for traditional anomaly detection systems to analyze and detect malicious network attacks. The increasing advancedness of cyber threats calls for innovative approaches to identify malicious patterns within network traffic precisely. The primary issue lies in the fact that these approaches do not focus on the essential adaptive features of network traffic. We proposed an effective anomaly detection system for malicious network traffic attacks called the Deep Ensemble Learning Model (DELM). We leverage the structure of the Feedforward Deep Neural Network (FDNN), and Deep Belief Network (DBN), incorporating multiple hidden layers with non-linear activation functions. Integrating Adaptive Feature Aggregation (AFA) with the FDNN algorithm dynamically adjusts the feature aggregation process based on incoming traffic characteristics to improve adaptability. The Conditional Generative Network was employed to enhance DELM for generating data for minority classes. To improve the model\u2019s accuracy, we applied batch normalization and data augmentation techniques for preprocessing, utilized n-gram, one-hot encoding, and feature aggregation methods for effective feature extraction. This study significantly contributes to network security by enhancing systems for detecting malicious network traffic. With its interpretability and adaptability, our proposed model shows promise in addressing the evolving cyber threat and fortifying critical network infrastructure. The experimental results demonstrate that our model performs with higher stability than the existing state-of-the-art detection approaches, as reflected by its higher accuracy, precision, recall, F1-score, and AUC-ROC.<\/jats:p>","DOI":"10.1145\/3690637","type":"journal-article","created":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T09:44:15Z","timestamp":1724924655000},"page":"1-36","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["DELM: Deep Ensemble Learning Model for Anomaly Detection in Malicious Network Traffic-based Adaptive Feature Aggregation and Network Optimization"],"prefix":"10.1145","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0167-5593","authenticated-orcid":false,"given":"Mukhtar","family":"Ahmed","sequence":"first","affiliation":[{"name":"School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, China and Quality Enhancement Cell, Lasbela University of Agriculture Water and Marine Sciences, Uthal, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3124-5452","authenticated-orcid":false,"given":"Jinfu","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, China and Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace, Jiangsu University, Zhenjiang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2540-3861","authenticated-orcid":false,"given":"Ernest","family":"Akpaku","sequence":"additional","affiliation":[{"name":"School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5527-5114","authenticated-orcid":false,"given":"Rexford Nii Ayitey","family":"Sosu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, China and Faculty of Computing and Information Systems, Ghana Communication Technology University, Accra, Ghana"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8050-4089","authenticated-orcid":false,"given":"Ajmal","family":"Latif","sequence":"additional","affiliation":[{"name":"Directorate of Information Technology, Lasbela University of Agriculture Water and Marine Sciences, Uthal, Pakistan"}]}],"member":"320","published-online":{"date-parts":[[2024,10,7]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3320042"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/ITAIC.2014.7065007"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.3390\/S23010550"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.COSE.2021.102225"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCCNT45670.2019.8944814"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/S11227-020-03447-Z\/FIGURES\/7"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2023.107166"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3613960"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP49357.2023.10096147"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.ESWA.2020.114520"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3087823"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/CCCS.2018.8586840"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/S00521-021-05993-W\/FIGURES\/6"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/S11704-019-8208-Z"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.ENGAPPAI.2022.105151"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.3390\/S20051452"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/S11063-020-10234-7\/TABLES\/3"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2008.4633969"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.NEUCOM.2015.04.105"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1155\/2023\/2989533"},{"key":"e_1_3_1_22_2","first-page":"2277","article-title":"UNSW-NB15 dataset feature selection and network intrusion detection using deep learning","volume":"7","author":"Kanimozhi V.","year":"2019","unstructured":"V. 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