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Using Trust to Establish a Secure Routing Model in Cognitive Radio Network.

Zhang G, Chen Z, Tian L, Zhang D - PLoS ONE (2015)

Bottom Line: At the same time, according to the trust classification, different responses are made specific to their service requests.By adopting stricter punishment on malicious behaviors from non-trusted nodes, the cooperation of nodes in routing can be stimulated.Simulation results and analysis indicate that this model has good performance in network throughput and end-to-end delay under the selective forwarding attack.

View Article: PubMed Central - PubMed

Affiliation: State Key Lab of Integrated Service Networks, Xidian University, Xi'an, Shannxi, China; College of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei, China.

ABSTRACT
Specific to the selective forwarding attack on routing in cognitive radio network, this paper proposes a trust-based secure routing model. Through monitoring nodes' forwarding behaviors, trusts of nodes are constructed to identify malicious nodes. In consideration of that routing selection-based model must be closely collaborative with spectrum allocation, a route request piggybacking available spectrum opportunities is sent to non-malicious nodes. In the routing decision phase, nodes' trusts are used to construct available path trusts and delay measurement is combined for making routing decisions. At the same time, according to the trust classification, different responses are made specific to their service requests. By adopting stricter punishment on malicious behaviors from non-trusted nodes, the cooperation of nodes in routing can be stimulated. Simulation results and analysis indicate that this model has good performance in network throughput and end-to-end delay under the selective forwarding attack.

No MeSH data available.


Mean trust evaluation of nodes.
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pone.0139326.g007: Mean trust evaluation of nodes.

Mentions: Nodes 1–30 in networks are selected, including malicious node 2, node 11, node 12, node 13, node 14, node 19, node 20, node 23, node 27 and other non-malicious nodes. The mean trust evaluations of all the neighboring nodes to those nodes are observed. It can be seen from Fig 7 that malicious nodes own significantly low trust evaluation relative to common nodes and trusted nodes and it is verified that the model can effectively identify malicious nodes through trust evaluation and further provide basis for the following routing node selection.


Using Trust to Establish a Secure Routing Model in Cognitive Radio Network.

Zhang G, Chen Z, Tian L, Zhang D - PLoS ONE (2015)

Mean trust evaluation of nodes.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4589414&req=5

pone.0139326.g007: Mean trust evaluation of nodes.
Mentions: Nodes 1–30 in networks are selected, including malicious node 2, node 11, node 12, node 13, node 14, node 19, node 20, node 23, node 27 and other non-malicious nodes. The mean trust evaluations of all the neighboring nodes to those nodes are observed. It can be seen from Fig 7 that malicious nodes own significantly low trust evaluation relative to common nodes and trusted nodes and it is verified that the model can effectively identify malicious nodes through trust evaluation and further provide basis for the following routing node selection.

Bottom Line: At the same time, according to the trust classification, different responses are made specific to their service requests.By adopting stricter punishment on malicious behaviors from non-trusted nodes, the cooperation of nodes in routing can be stimulated.Simulation results and analysis indicate that this model has good performance in network throughput and end-to-end delay under the selective forwarding attack.

View Article: PubMed Central - PubMed

Affiliation: State Key Lab of Integrated Service Networks, Xidian University, Xi'an, Shannxi, China; College of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei, China.

ABSTRACT
Specific to the selective forwarding attack on routing in cognitive radio network, this paper proposes a trust-based secure routing model. Through monitoring nodes' forwarding behaviors, trusts of nodes are constructed to identify malicious nodes. In consideration of that routing selection-based model must be closely collaborative with spectrum allocation, a route request piggybacking available spectrum opportunities is sent to non-malicious nodes. In the routing decision phase, nodes' trusts are used to construct available path trusts and delay measurement is combined for making routing decisions. At the same time, according to the trust classification, different responses are made specific to their service requests. By adopting stricter punishment on malicious behaviors from non-trusted nodes, the cooperation of nodes in routing can be stimulated. Simulation results and analysis indicate that this model has good performance in network throughput and end-to-end delay under the selective forwarding attack.

No MeSH data available.