{"title":"基于机器学习算法的电力信息网络安全态势感知","authors":"Chao Wang, Jia-han Dong, Guang-xin Guo, Tian-yu Ren, Xiao-hu Wang, Ming-yu Pan","doi":"10.1080/09540091.2023.2284649","DOIUrl":null,"url":null,"abstract":"To properly predict the security posture of these networks, we provide a method based on machine learning algorithms to detect the security condition of power information networks. A perception mod...","PeriodicalId":50629,"journal":{"name":"Connection Science","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Security situational awareness of power information networks based on machine learning algorithms\",\"authors\":\"Chao Wang, Jia-han Dong, Guang-xin Guo, Tian-yu Ren, Xiao-hu Wang, Ming-yu Pan\",\"doi\":\"10.1080/09540091.2023.2284649\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To properly predict the security posture of these networks, we provide a method based on machine learning algorithms to detect the security condition of power information networks. A perception mod...\",\"PeriodicalId\":50629,\"journal\":{\"name\":\"Connection Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2023-11-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Connection Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/09540091.2023.2284649\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Connection Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/09540091.2023.2284649","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Security situational awareness of power information networks based on machine learning algorithms
To properly predict the security posture of these networks, we provide a method based on machine learning algorithms to detect the security condition of power information networks. A perception mod...
期刊介绍：
Connection Science is an interdisciplinary journal dedicated to exploring the convergence of the analytic and synthetic sciences, including neuroscience, computational modelling, artificial intelligence, machine learning, deep learning, Database, Big Data, quantum computing, Blockchain, Zero-Knowledge, Internet of Things, Cybersecurity, and parallel and distributed computing.
A strong focus is on the articles arising from connectionist, probabilistic, dynamical, or evolutionary approaches in aspects of Computer Science, applied applications, and systems-level computational subjects that seek to understand models in science and engineering.
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