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Therefore, effectively identifying network communication data is essential to protect data communication security between individual terminals and the cloud server. Aiming at the data mentioned above regarding communication security issues, we propose an intelligent intrusion detection model NIDD (Network Intelligent Data Detection) model that combines deep convolution generation adversarial network (DCGAN) with Light Gradient Boosting Machine (LightGBM) and Shapley Additive exPlanations (SHAP). The NIDD model first generates new attack samples by learning the feature distribution of the existing attack sample data and effectively expands the rare attack samples. Secondly, we use the Light Gradient Boosting Machine (LightGBM) algorithm as the base classifier to train the dataset and start to build the intrusion detection model. Then use Shapley Additive exPlanations (SHAP) to analyze the contribution of the classification results, and adjust the model parameters according to the analysis results. Finally, we obtain the optimal model for the intelligent detection model of network intrusion. This paper conducts experimental tests on the NSL-KDD dataset. The experimental results show that the NIDD model built based on Light Gradient Boosting Machine can detect Brute Force FTP, Brute Force SSH, DoS, Heartbleed, Web Attack, Infiltration, Botnet, PROBE, R2L, and U2R attacks with an accuracy of 99.76%. Finally, we re-verified the NIDD model on the CIC-IDC-2018 dataset. The results once again proved that the NIDD model could solve the data communication security between the nursing robot and the cloud server and the data before the IoT terminal and the cloud server. Communication security provides a sufficient guarantee.<\/jats:p>","DOI":"10.1186\/s13677-022-00361-y","type":"journal-article","created":{"date-parts":[[2022,12,8]],"date-time":"2022-12-08T12:02:59Z","timestamp":1670500979000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["NIDD: an intelligent network intrusion detection model for nursing homes"],"prefix":"10.1186","volume":"11","author":[{"given":"Feng","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Du","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenli","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhihui","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,8]]},"reference":[{"issue":"11","key":"361_CR1","first-page":"13","volume":"163","author":"B Chakrabarty","year":"2017","unstructured":"Chakrabarty B, Chanda O, Islam S (2017) Anomaly based intrusion detection system using genetic algorithm and Kcentroid clustering[J]. 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By submitting my article I agree to pay this charge in full if my article is accepted for publication. I declare that the authors have no competing interests as defined by Springer, or other interests that might be perceived to influence the results and\/or discussion reported in this paper. The results\/data\/figures in this manuscript have not been published elsewhere, nor are they under consideration (from you or one of your Contributing Authors) by another publisher. I have read the Springer journal policies on author responsibilities and submit this manuscript in accordance with those policies. All of the material is owned by the authors and\/or no permissions are required. I am the author responsible for the submission of this article and I accept the conditions of submission and the Springer Copyright and License Agreement as detailed above.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"I declare that the authors have no competing interests as defined by Springer, or other interests that might be perceived to influence the results and\/or discussion reported in this paper.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"91"}}