PUE Attack Detection in CWSN Using Collaboration and Learning Behavior
Cognitive Wireless Sensor Network (CWSN) is a new paradigm which integrates cognitive features in traditional Wireless Sensor Networks (WSNs) to mitigate important problems such as spectrum occupancy. Security in Cognitive Wireless Sensor Networks is an important problem because these kinds of netwo...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-06-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2013/815959 |
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| _version_ | 1849699777183219712 |
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| author | Javier Blesa Elena Romero Alba Rozas Alvaro Araujo Octavio Nieto-Taladriz |
| author_facet | Javier Blesa Elena Romero Alba Rozas Alvaro Araujo Octavio Nieto-Taladriz |
| author_sort | Javier Blesa |
| collection | DOAJ |
| description | Cognitive Wireless Sensor Network (CWSN) is a new paradigm which integrates cognitive features in traditional Wireless Sensor Networks (WSNs) to mitigate important problems such as spectrum occupancy. Security in Cognitive Wireless Sensor Networks is an important problem because these kinds of networks manage critical applications and data. Moreover, the specific constraints of WSN make the problem even more critical. However, effective solutions have not been implemented yet. Among the specific attacks derived from new cognitive features, the one most studied is the Primary User Emulation (PUE) attack. This paper discusses a new approach, based on anomaly behavior detection and collaboration, to detect the PUE attack in CWSN scenarios. A nonparametric CUSUM algorithm, suitable for low resource networks like CWSN, has been used in this work. The algorithm has been tested using a cognitive simulator that brings important results in this area. For example, the result shows that the number of collaborative nodes is the most important parameter in order to improve the PUE attack detection rates. If the 20% of the nodes collaborates, the PUE detection reaches the 98% with less than 1% of false positives. |
| format | Article |
| id | doaj-art-2e94e7785a3a4a2da71fae9a24c1e6fc |
| institution | DOAJ |
| issn | 1550-1477 |
| language | English |
| publishDate | 2013-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-2e94e7785a3a4a2da71fae9a24c1e6fc2025-08-20T03:18:28ZengWileyInternational Journal of Distributed Sensor Networks1550-14772013-06-01910.1155/2013/815959PUE Attack Detection in CWSN Using Collaboration and Learning BehaviorJavier BlesaElena RomeroAlba RozasAlvaro AraujoOctavio Nieto-TaladrizCognitive Wireless Sensor Network (CWSN) is a new paradigm which integrates cognitive features in traditional Wireless Sensor Networks (WSNs) to mitigate important problems such as spectrum occupancy. Security in Cognitive Wireless Sensor Networks is an important problem because these kinds of networks manage critical applications and data. Moreover, the specific constraints of WSN make the problem even more critical. However, effective solutions have not been implemented yet. Among the specific attacks derived from new cognitive features, the one most studied is the Primary User Emulation (PUE) attack. This paper discusses a new approach, based on anomaly behavior detection and collaboration, to detect the PUE attack in CWSN scenarios. A nonparametric CUSUM algorithm, suitable for low resource networks like CWSN, has been used in this work. The algorithm has been tested using a cognitive simulator that brings important results in this area. For example, the result shows that the number of collaborative nodes is the most important parameter in order to improve the PUE attack detection rates. If the 20% of the nodes collaborates, the PUE detection reaches the 98% with less than 1% of false positives.https://doi.org/10.1155/2013/815959 |
| spellingShingle | Javier Blesa Elena Romero Alba Rozas Alvaro Araujo Octavio Nieto-Taladriz PUE Attack Detection in CWSN Using Collaboration and Learning Behavior International Journal of Distributed Sensor Networks |
| title | PUE Attack Detection in CWSN Using Collaboration and Learning Behavior |
| title_full | PUE Attack Detection in CWSN Using Collaboration and Learning Behavior |
| title_fullStr | PUE Attack Detection in CWSN Using Collaboration and Learning Behavior |
| title_full_unstemmed | PUE Attack Detection in CWSN Using Collaboration and Learning Behavior |
| title_short | PUE Attack Detection in CWSN Using Collaboration and Learning Behavior |
| title_sort | pue attack detection in cwsn using collaboration and learning behavior |
| url | https://doi.org/10.1155/2013/815959 |
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