| Autor: |
Zhao Xuezhi, Lin Liangcheng, Guo Tao, Du Jinbao, Feng Baozhan |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Applied Mathematics and Nonlinear Sciences, Vol 8, Iss 1, Pp 2835-2848 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2444-8656 |
| DOI: |
10.2478/amns.2023.1.00004 |
| Popis: |
With the progress of the times, big data will follow the development of the Internet and show a broader development space. But the characteristics of big data itself also make it more difficult to solve the problems of network attacks and theft of sensitive information. Therefore, it is more important to conduct in-depth research on network information security in many fields in the context of big data. In this paper, we build a DFN-Big Data network model based on deep feed-forward network as the algorithm, and conduct an in-depth study on network information security. The calculation results show that, among several leakage methods of personal information, the leakage caused by hacking is the most dangerous and unpredictable. The percentage of cyber incidents caused by hacking is 43%. The network security problem caused by excessive collection of personal user information is also very serious, and its percentage is 34%. Establishing a sound legal regulatory system can effectively reduce the occurrence of network information leakage. Compared with other security technology solutions, a sound legal regulatory system increases network information security by 67%. Information protection technology improves network information security by 87%. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
