Name
Abstract | ||
|---|---|---|
A large amount of real-time data, including user privacy information, control commands, and other sensitive data, are transmitted in edge computing networks. It requires high-speed and reliable data transmission in dynamic edge computing networks. Traditional methods with passive defense cannot cope with the covert and complicated attacks. Edge computing networks require active defense during data transmission. Existing active defense methods based on dynamic network ignore the connectivity and link quality reduced by attacks and do not adjust defense positively. To maximize the defense revenue in moving adjustment strategy, this paper proposes the model of active defense for edge computing network data interaction. In this model, the network topology mimic association protocol is designed to associate multi-paths and multi-parameters automatically. On one hand, considering the transmission reliability and defensive revenue reduction caused by dynamic network transformation, a real-time multi-feature anomaly detection algorithm based on Non-extensive entropy and Renyi cross entropy is proposed. Based on this, a moving communication path alliance can be constructed pseudo-randomly. On the other hand, this paper proposes a Hidden Markov based state prediction model and a mimic transformation strategy for The Network Topology Mimic Association Graph based on predicted states. Combining these two ways improves the data transmission service quality of the active defense technology in edge computing networks. Experiments are carried in simulated power networks. The results show that our method outperforms the popular methods in terms of transmission efficiency, reliability, and anti-attack performance. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s11036-019-01446-w | Mobile Networks and Applications |
Keywords | DocType | Volume |
Network attack, Active defense, Dynamic network, Edge computing | Journal | 25 |
Issue | ISSN | Citations |
2 | 1383-469X | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuo Wang | 1 | 303 | 54.05 |
| Li Qian-Mu | 2 | 33 | 14.78 |
| Jun Hou | 3 | 4 | 4.14 |
| Shunmei Meng | 4 | 33 | 5.34 |
| Bo Zhang | 5 | 0 | 0.34 |
| Cangqi Zhou | 6 | 2 | 3.42 |