{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:49:09Z","timestamp":1760150949743,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,2,7]],"date-time":"2022-02-07T00:00:00Z","timestamp":1644192000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100015974","name":"State Grid Henan Electric Power Company","doi-asserted-by":"publisher","award":["SGHAXTOOYJJS2100034"],"award-info":[{"award-number":["SGHAXTOOYJJS2100034"]}],"id":[{"id":"10.13039\/501100015974","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>With the rapid development and wide application of cloud computing, security protection in cloud environment has become an urgent problem to be solved. However, traditional security service equipment is closely coupled with the network topology, so it is difficult to upgrade and expand the security service, which cannot change with the change of network application security requirements. Building a security service function chain (SSFC) makes the deployment of security service functions more dynamic and scalable. Based on a software defined network (SDN) and network function virtualization (NFV) environment, this paper proposes a solution to the particularity optimization algorithm of network topology feature extraction using graph neural network. The experimental results show that, compared with the shortest path, greedy algorithm and hybrid bee colony algorithm, the average success rate of the graph neural network algorithm in the construction of the security service function chain is more than 90%, far more than other algorithms, and far less than other algorithms in construction time. It effectively reduces the end-to-end delay and increases the network throughput.<\/jats:p>","DOI":"10.3390\/info13020078","type":"journal-article","created":{"date-parts":[[2022,2,7]],"date-time":"2022-02-07T20:36:42Z","timestamp":1644266202000},"page":"78","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Security Service Function Chain Based on Graph Neural Network"],"prefix":"10.3390","volume":"13","author":[{"given":"Wei","family":"Li","sequence":"first","affiliation":[{"name":"School of Control and Computer Engineering, North China Electric Power University, No. 2 Beinong Road, Changping District, Beijing 102206, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haomin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Control and Computer Engineering, North China Electric Power University, No. 2 Beinong Road, Changping District, Beijing 102206, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1531-8946","authenticated-orcid":false,"given":"Xiaoliang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Control and Computer Engineering, North China Electric Power University, No. 2 Beinong Road, Changping District, Beijing 102206, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dingding","family":"Li","sequence":"additional","affiliation":[{"name":"The State Grid Henan Information & Communication Company, Zhengzhou 450052, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lijing","family":"Yan","sequence":"additional","affiliation":[{"name":"The State Grid Henan Information & Communication Company, Zhengzhou 450052, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3620-8201","authenticated-orcid":false,"given":"Qi","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Control and Computer Engineering, North China Electric Power University, No. 2 Beinong Road, Changping District, Beijing 102206, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Control and Computer Engineering, North China Electric Power University, No. 2 Beinong Road, Changping District, Beijing 102206, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruoyu","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Control and Computer Engineering, North China Electric Power University, No. 2 Beinong Road, Changping District, Beijing 102206, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,7]]},"reference":[{"key":"ref_1","first-page":"36","article-title":"SDN-based data center network flow probabilistic path selection method","volume":"45","author":"Zhao","year":"2019","journal-title":"Comput. 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