A Malicious Node Identification Strategy with Environmental Parameters Optimization in Wireless Sensor Network
| Autor: | Liwen Guo, Luying Xie, Mingyang Sun, Zhijun Teng, Baohe Pang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry media_common.quotation_subject Reliability (computer networking) Volume (computing) 020206 networking & telecommunications Eavesdropping 02 engineering and technology Computer Science Applications Identification (information) Node (computer science) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing State (computer science) Electrical and Electronic Engineering business Wireless sensor network Reputation media_common Computer network |
| Zdroj: | Wireless Personal Communications. 117:1143-1162 |
| ISSN: | 1572-834X 0929-6212 |
| Popis: | In the wireless sensor network (WSN), nodes show a low forwarding rate under a poor-quality links environment and a resource-constrained state. The malicious nodes imitate this forwarding behavior, which can selectively forward date, eavesdropping, or discarding important dates. The traditional reputation model is challenging to identify with this kind of sub-attack nodes. To address these problems, a malicious node identification strategy based on time reputation model and environmental parameters optimization (TRM-EPO) is proposed in the WSN. First of all, the comprehensive reputation is calculated according to the direct reputation and the recommended indirect reputation. The environmental parameters matrix is based on nodes’ running state, taking into account nodes’ energy, data volume, number of adjacent nodes, and node sparsity. Besides, according to the environmental parameters matrix, and the recorded comprehensive reputation matrix, the next cycle’s trust can be predicted. Finally, a similarity of the actual reputation and predicted trust matrix is proposed to compare with an adaptive threshold to identify malicious nodes. The experimental results demonstrate that the proposed strategy improves sensor nodes’ security and reliability in a complex environment. Moreover, compared to comparison algorithms, the TRM-EPO improves the recognition rate above 1% and reduces the false-positive rate by more than 1%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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