| Autor: |
Yao Yu, Lei Guo, Jinli Huang, Fengyan Zhang, Yue Zong |
| Jazyk: |
angličtina |
| Rok vydání: |
2018 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 6, Pp 35382-35391 (2018) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2018.2851993 |
| Popis: |
Intrusion detection system (IDS) is emerging as a technology to improve the security of vehicular ad hoc network. However, the recent research on performance of IDS indicates that monitoring overhead is still a significant issue and challenge. The fundamental problem is caused by the fact that each node repeatedly detects adjacent nodes during operation. At the same time, monitoring results are sent to other nodes in the form of control packets, resulting in the decline of the network throughput. In this paper, we focus on selecting a bunch of monitoring nodes and propose a cross-layer security monitoring selection algorithm based on traffic prediction (CLSM-TP). Instead of repeatedly detection for each node, we select the monitoring node whose idle degree is relatively high by predicting the node's traffic based on a cross-layer vehicular ad hoc network. Moreover, the proposed algorithm leverages the mutual information and residual energy to optimize the node selection through social network analysis. The noteworthy contributions are that CLSM-TP can balance the resource consumption among all nodes and prolong the lifetime of vehicular ad hoc network to some extent. Our experimental results show that, the monitoring nodes selected by the algorithm proposed in this paper with higher idle degree preform good enough to monitor the whole vehicular ad hoc network. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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