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However, a significant issue in the anomaly detection is the insufficient labels of anomaly classes. With emergence of the new and unknown network attacks, accurately labeling these attacks can be a costly task. The issue of inadequate labeling may negatively impact the detection performance of many existing anomaly detection methods. To meet this gap, this paper proposes a semi-supervised collaborative learning paradigm called IDG-SemiAD, based on an immune detector generation algorithm. First, we design an immune detector generation algorithm based on a chaos map to generate abnormal samples from self-samples. Then, these abnormal samples are combined with self-samples and given specific labels to form a new training set. Finally, the LightGBM classifier is used for training and detection. Experiments on the widely used public dataset BATADAL show that the proposed IDG-SemiAD outperforms the classical v-detector method in terms of recall and f-score, with improvements of 8.2% and 8%, respectively, and outperforms deep learning-based anomaly detection methods, with a maximum improvements of up to 89.7% and 59.5% respectively.<\/jats:p>","DOI":"10.1007\/s44196-024-00498-5","type":"journal-article","created":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T10:02:05Z","timestamp":1714384925000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["IDG-SemiAD: An Immune Detector Generation-Based Collaborative Learning Scheme for Semi-supervised Anomaly Detection in Industrial Cyber-physical Systems"],"prefix":"10.1007","volume":"17","author":[{"given":"Meng","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenshan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junjiang","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolong","family":"Lan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nian","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,4,29]]},"reference":[{"key":"498_CR1","doi-asserted-by":"publisher","first-page":"1287","DOI":"10.1109\/JPROC.2012.2189792","volume":"100","author":"KD Kim","year":"2012","unstructured":"Kim, K.D., Kumar, P.R.: Cyber-physical systems: a perspective at the centennial. 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