{"title":"Analysis and Countermeasures of Computer Network Security in the Age of Artificial Intelligence","authors":"Yunpeng Lu","doi":"10.56397/ist.2023.11.02","DOIUrl":null,"url":null,"abstract":"With the widespread adoption of artificial intelligence technology, the landscape of network threats is continually evolving. Malicious attackers are increasingly leveraging intelligent technology to enhance their attacks, resulting in more intricate network security challenges. Among these challenges, antagonistic attacks and intelligent threats stand out, while the risk of large-scale data breaches and privacy infringements looms over both individuals and organizations. This paper proposes a set of strategies to address these pressing issues. These strategies encompass the deployment of deep learning technology, safeguarding data privacy, the dissemination of automation solutions, and the advancement of network user education. Furthermore, the importance of research into antagonistic attacks and emerging technologies, as well as the significance of international cooperation and information sharing, are underscored to ensure the resilience of network security in the face of evolving threats. This comprehensive approach serves as a valuable resource for safeguarding the information security of network users and fostering the prosperity and sustainable development of our digital society.","PeriodicalId":20688,"journal":{"name":"Proceedings of The 6th International Conference on Innovation in Science and Technology","volume":"86 12","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of The 6th International Conference on Innovation in Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.56397/ist.2023.11.02","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
With the widespread adoption of artificial intelligence technology, the landscape of network threats is continually evolving. Malicious attackers are increasingly leveraging intelligent technology to enhance their attacks, resulting in more intricate network security challenges. Among these challenges, antagonistic attacks and intelligent threats stand out, while the risk of large-scale data breaches and privacy infringements looms over both individuals and organizations. This paper proposes a set of strategies to address these pressing issues. These strategies encompass the deployment of deep learning technology, safeguarding data privacy, the dissemination of automation solutions, and the advancement of network user education. Furthermore, the importance of research into antagonistic attacks and emerging technologies, as well as the significance of international cooperation and information sharing, are underscored to ensure the resilience of network security in the face of evolving threats. This comprehensive approach serves as a valuable resource for safeguarding the information security of network users and fostering the prosperity and sustainable development of our digital society.