Visible to the public Analysis of Computer Network Information Security under the Background of Big Data

TitleAnalysis of Computer Network Information Security under the Background of Big Data
Publication TypeConference Paper
Year of Publication2020
AuthorsPeng, X., Hongmei, Z., Lijie, C., Ying, H.
Conference Name2020 5th International Conference on Smart Grid and Electrical Automation (ICSGEA)
KeywordsBig Data, big data acquisition, big data analysis platform, Big Data technology, Collaboration, composability, comprehensive arrival, computer network information security, computer network security, Conferences, data acquisition, Data analysis, data subject, distributed computing, Frequency locked loops, Hafnium compounds, Human Behavior, information assurance, information security assurance services, information system, Internet, Internet era, large-scale network environment, large-scale network security situational awareness, Metrics, mining, Network security, OWL, parallel computing, people, policy-based governance, pubcrawl, Quality function deployment, resilience, Resiliency, Scalability, security perception, situation awareness, Smart grids, Xenon
AbstractIn today's society, under the comprehensive arrival of the Internet era, the rapid development of technology has facilitated people's production and life, but it is also a “double-edged sword”, making people's personal information and other data subject to a greater threat of abuse. The unique features of big data technology, such as massive storage, parallel computing and efficient query, have created a breakthrough opportunity for the key technologies of large-scale network security situational awareness. On the basis of big data acquisition, preprocessing, distributed computing and mining and analysis, the big data analysis platform provides information security assurance services to the information system. This paper will discuss the security situational awareness in large-scale network environment and the promotion of big data technology in security perception.
DOI10.1109/ICSGEA51094.2020.00094
Citation Keypeng_analysis_2020