{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T14:21:52Z","timestamp":1773325312441,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,9,7]],"date-time":"2023-09-07T00:00:00Z","timestamp":1694044800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Aiming at the problem of poor detection performance under the environment of imbalanced type distribution, an intrusion detection model of genetic algorithm to optimize weighted extreme learning machine based on stratified cross-validation (SCV-GA-WELM) is proposed. In order to solve the problem of imbalanced data types in cross-validation subsets, SCV is used to ensure that the data distribution in all subsets is consistent, thus avoiding model over-fitting. The traditional fitness function cannot solve the problem of small sample classification well. By designing a weighted fitness function and giving high weight to small sample data, the performance of the model can be effectively improved in the environment of imbalanced type distribution. The experimental results show that this model is superior to other intrusion detection models in recall and McNemar hypothesis test. In addition, the recall of the model for small sample data is higher, reaching 91.5% and 95.1%, respectively. This shows that it can effectively detect intrusions in an environment with imbalanced type distribution. Therefore, the model has practical application value in the field of intrusion detection, and can be used to improve the performance of intrusion detection systems in the actual environment. This method has a wide application prospect, such as network security, industrial control system, and power system.<\/jats:p>","DOI":"10.3390\/sym15091719","type":"journal-article","created":{"date-parts":[[2023,9,8]],"date-time":"2023-09-08T07:52:11Z","timestamp":1694159531000},"page":"1719","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Application of GA-WELM Model Based on Stratified Cross-Validation in Intrusion Detection"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7081-5668","authenticated-orcid":false,"given":"Chen","family":"Chen","sequence":"first","affiliation":[{"name":"China Xi\u2019an Satellite Control Center, Xi\u2019an 710043, China"}]},{"given":"Xiangke","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Air and Missile Defense, Air Force Engineering University, Xi\u2019an 710051, China"}]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"China Xi\u2019an Satellite Control Center, Xi\u2019an 710043, China"}]},{"given":"Yanzhao","family":"Zhao","sequence":"additional","affiliation":[{"name":"China Xi\u2019an Satellite Control Center, Xi\u2019an 710043, China"}]},{"given":"Biao","family":"Wang","sequence":"additional","affiliation":[{"name":"China Xi\u2019an Satellite Control Center, Xi\u2019an 710043, China"}]},{"given":"Biao","family":"Ma","sequence":"additional","affiliation":[{"name":"China Xi\u2019an Satellite Control Center, Xi\u2019an 710043, China"}]},{"given":"Dan","family":"Wei","sequence":"additional","affiliation":[{"name":"China Xi\u2019an Satellite Control Center, Xi\u2019an 710043, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1079483","DOI":"10.3389\/fncom.2023.1079483","article-title":"Kohonen neural network and symbiotic-organism search algorithm for intrusion detection of network viruses","volume":"17","author":"Zhou","year":"2023","journal-title":"Front. 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