Name
Abstract | ||
|---|---|---|
Online social network users provide considerable amounts of personal information and share this information with friends without space-time limitations. The tight connectivity among users of social networks causes the rapid spreading of information. Given the popularity of social networking sites, there is a high probability of attacks. Worms target popular users with interesting information to infect them, as their higher reputations have more power in social networks. Therefore, timely patch propagation schemes must be able to inhibit the activity of worms. To improve the patch propagation speed, it is important to select key nodes that are the starting points of the patch process. In this paper, we proposed a key node selection scheme based on a genetic algorithm to find the most significant contribution nodes of patch propagation. We modeled the usage patterns of an online social network user and simulated the proposed scheme with data from this user. Simulation results show that the proposed scheme propagates patches more rapidly than existing schemes. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1002/cpe.5194 | CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE |
Keywords | DocType | Volume |
genetic algorithm, key node selection, patching, social networks | Journal | 33 |
Issue | ISSN | Citations |
2 | 1532-0626 | 0 |
PageRank | References | Authors |
0.34 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bongjae Kim | 1 | 4 | 2.25 |
| Jinman Jung | 2 | 24 | 14.63 |
| Junyoung Heo | 3 | 288 | 27.60 |
| Hong Min | 4 | 57 | 13.34 |