| Title | Automatic patch installation method of operating system based on deep learning |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Zhang, KunSan, Chen, Chen, Lin, Nan, Zeng, Zhen, Fu, ShiChen |
| Conference Name | 2021 IEEE 5th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) |
| Keywords | Costs, Deep Learning, Industries, Operating system patches, Operating systems, Operating Systems Security, Patch detection visualization, Power systems, pubcrawl, resilience, Resiliency, security, visualization |
| Abstract | In order to improve the security and reliability of information system and reduce the risk of vulnerability intrusion and attack, an automatic patch installation method of operating systems based on deep learning is proposed, If the installation is successful, the basic information of the system will be returned to the visualization server. If the installation fails, it is recommended to upgrading manually and display it on the patch detection visualization server. Through the practical application of statistical analysis, the statistical results show that the proposed method is significantly better than the original and traditional installation methods, which can effectively avoid the problem of client repeated download, and greatly improve the success rate of patch automatic upgrades. It effectively saves the upgrade cost and ensures the security and reliability of the information system. |
| DOI | 10.1109/ITNEC52019.2021.9586941 |
| Citation Key | zhang_automatic_2021 |
Automatic patch installation method of operating system based on deep learning