{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T17:26:41Z","timestamp":1783099601066,"version":"3.54.6"},"reference-count":40,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,7,6]],"date-time":"2023-07-06T00:00:00Z","timestamp":1688601600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Henan Programs for Science and Technology Development","award":["212102210100"],"award-info":[{"award-number":["212102210100"]}]},{"name":"Henan Programs for Science and Technology Development","award":["2021SJGLX581"],"award-info":[{"award-number":["2021SJGLX581"]}]},{"name":"Henan Province Higher Education Teaching Reform Research and Practice Project","award":["212102210100"],"award-info":[{"award-number":["212102210100"]}]},{"name":"Henan Province Higher Education Teaching Reform Research and Practice Project","award":["2021SJGLX581"],"award-info":[{"award-number":["2021SJGLX581"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The development of internet technology has brought us benefits, but at the same time, there has been a surge in network attack incidents, posing a serious threat to network security. In the real world, the amount of attack data is much smaller than normal data, leading to a severe class imbalance problem that affects the performance of classifiers. Additionally, when using CNN for detection and classification, manual adjustment of parameters is required, making it difficult to obtain the optimal number of convolutional kernels. Therefore, we propose a hybrid sampling technique called Borderline-SMOTE and Gaussian Mixture Model (GMM), referred to as BSGM, which combines the two approaches. We utilize the Quantum Particle Swarm Optimization (QPSO) algorithm to automatically determine the optimal number of convolutional kernels for each one-dimensional convolutional layer, thereby enhancing the detection rate of minority classes. In our experiments, we conducted binary and multi-class experiments using the KDD99 dataset. We compared our proposed BSGM-QPSO-1DCNN method with ROS-CNN, SMOTE-CNN, RUS-SMOTE-CNN, RUS-SMOTE-RF, and RUS-SMOTE-MLP as benchmark models for intrusion detection. The experimental results show the following: (i) BSGM-QPSO-1DCNN achieves high accuracy rates of 99.93% and 99.94% in binary and multi-class experiments, respectively; (ii) the precision rates for the minority classes R2L and U2R are improved by 68% and 66%, respectively. Our research demonstrates that BSGM-QPSO-1DCNN is an efficient solution for addressing the imbalanced data issue in this field, and it outperforms the five intrusion detection methods used in this study.<\/jats:p>","DOI":"10.3390\/s23136206","type":"journal-article","created":{"date-parts":[[2023,7,7]],"date-time":"2023-07-07T01:57:09Z","timestamp":1688695029000},"page":"6206","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Research on Adaptive 1DCNN Network Intrusion Detection Technology Based on BSGM Mixed Sampling"],"prefix":"10.3390","volume":"23","author":[{"given":"Wei","family":"Ma","sequence":"first","affiliation":[{"name":"School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Gou","sequence":"additional","affiliation":[{"name":"School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunyun","family":"Hou","sequence":"additional","affiliation":[{"name":"School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2671","DOI":"10.1109\/COMST.2019.2896380","article-title":"Network Intrusion Detection for IoT Security Based on Learning Techniques","volume":"21","author":"Chaabouni","year":"2019","journal-title":"IEEE Commun. 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