Name
Title | ||
|---|---|---|
Identifying malicious nodes in wireless sensor networks based on correlation detection |
Abstract | ||
|---|---|---|
The wireless sensor network (WSN) is a multi-hop wireless network that comprises multiple sensor nodes arranged in a self-organized manner. It is usually deployed in unattended areas where sensor nodes can easily be infiltrated by attackers who can affect the detection results by injecting false data. This paper proposes a malicious-node identification method based on correlation theory that prevents fault data injection attacks. First, anomalies among similar types of sensor data are detected based on time correlation. Second, malicious nodes are identified based on spatial correlation. Third, the identified malicious nodes are verified based on event correlation. The experimental results and their comparison with those of existing methods show that the proposed scheme has better recall with lower false-positive and false-negative rates than those of the traditional fuzzy reputation model and weighted-trust-based methods. (C) 2021 The Authors. Published by Elsevier Ltd. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.cose.2021.102540 | COMPUTERS & SECURITY |
Keywords | DocType | Volume |
Autoregressive integrated moving-average model, Correlation coefficient, Correlation theory, Dempster-Shafe evidential reasoning, False data injection attacks, Wireless sensor network | Journal | 113 |
ISSN | Citations | PageRank |
0167-4048 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ying-xu. Lai | 1 | 37 | 13.05 |
| Liyao Tong | 2 | 0 | 0.34 |
| Jing Liu | 3 | 1043 | 115.54 |
| Yipeng Wang | 4 | 0 | 0.34 |
| Tong Tang | 5 | 0 | 0.34 |
| Zijian Zhao | 6 | 0 | 0.34 |
| Hua Qin | 7 | 0 | 0.34 |