Toward Intelligent Intrusion Prediction for Wireless Sensor Networks Using Three-Layer Brain-Like Learning
The intrusion prediction for wireless sensor networks (WSNs) is an unresolved problem. Hence, the current intrusion detection schemes cannot provide enough security for WSNs, which poses a number of security challenges in WSNs. In many mission-critical applications, such as battle field, even though...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2012-10-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2012/243841 |
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| _version_ | 1832556797219569664 |
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| author | Jun Wu Song Liu Zhenyu Zhou Ming Zhan |
| author_facet | Jun Wu Song Liu Zhenyu Zhou Ming Zhan |
| author_sort | Jun Wu |
| collection | DOAJ |
| description | The intrusion prediction for wireless sensor networks (WSNs) is an unresolved problem. Hence, the current intrusion detection schemes cannot provide enough security for WSNs, which poses a number of security challenges in WSNs. In many mission-critical applications, such as battle field, even though the intrusion detection systems (IDSs) without prediction capability could detect the malicious activities afterwards, the damages to the WSNs have been generated and could hardly be restored. In addition, sensor nodes usually are resource constrained, which limits the direct adoption of expensive intrusion prediction algorithm. To address the above challenges, we propose an intelligent intrusion prediction scheme that is able to enforce accurate intrusion prediction. The proposed scheme exploits a novel three-layer brain-like hierarchical learning framework, tailors, and adapts it for WSNs with both performance and security requirements. The implementation system of the proposed scheme is designed based on agent technology. Moreover, an attack experiment is done for getting training and test data set. Experiment results show that the proposed scheme has several advantages in terms of efficiency of implementation and high prediction rate. To our best knowledge, this paper is the first to realize intrusion prediction for WSNs. |
| format | Article |
| id | doaj-art-1864ca053cc04ef58c3d3f0e5abc252f |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2012-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-1864ca053cc04ef58c3d3f0e5abc252f2025-02-03T05:44:18ZengWileyInternational Journal of Distributed Sensor Networks1550-14772012-10-01810.1155/2012/243841Toward Intelligent Intrusion Prediction for Wireless Sensor Networks Using Three-Layer Brain-Like LearningJun Wu0Song Liu1Zhenyu Zhou2Ming Zhan3 Global Information and Telecommunication Institute, Waseda University, Tokyo 169-0051, Japan Global Information and Telecommunication Institute, Waseda University, Tokyo 169-0051, Japan Global Information and Telecommunication Institute, Waseda University, Tokyo 169-0051, Japan National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu 611731, ChinaThe intrusion prediction for wireless sensor networks (WSNs) is an unresolved problem. Hence, the current intrusion detection schemes cannot provide enough security for WSNs, which poses a number of security challenges in WSNs. In many mission-critical applications, such as battle field, even though the intrusion detection systems (IDSs) without prediction capability could detect the malicious activities afterwards, the damages to the WSNs have been generated and could hardly be restored. In addition, sensor nodes usually are resource constrained, which limits the direct adoption of expensive intrusion prediction algorithm. To address the above challenges, we propose an intelligent intrusion prediction scheme that is able to enforce accurate intrusion prediction. The proposed scheme exploits a novel three-layer brain-like hierarchical learning framework, tailors, and adapts it for WSNs with both performance and security requirements. The implementation system of the proposed scheme is designed based on agent technology. Moreover, an attack experiment is done for getting training and test data set. Experiment results show that the proposed scheme has several advantages in terms of efficiency of implementation and high prediction rate. To our best knowledge, this paper is the first to realize intrusion prediction for WSNs.https://doi.org/10.1155/2012/243841 |
| spellingShingle | Jun Wu Song Liu Zhenyu Zhou Ming Zhan Toward Intelligent Intrusion Prediction for Wireless Sensor Networks Using Three-Layer Brain-Like Learning International Journal of Distributed Sensor Networks |
| title | Toward Intelligent Intrusion Prediction for Wireless Sensor Networks Using Three-Layer Brain-Like Learning |
| title_full | Toward Intelligent Intrusion Prediction for Wireless Sensor Networks Using Three-Layer Brain-Like Learning |
| title_fullStr | Toward Intelligent Intrusion Prediction for Wireless Sensor Networks Using Three-Layer Brain-Like Learning |
| title_full_unstemmed | Toward Intelligent Intrusion Prediction for Wireless Sensor Networks Using Three-Layer Brain-Like Learning |
| title_short | Toward Intelligent Intrusion Prediction for Wireless Sensor Networks Using Three-Layer Brain-Like Learning |
| title_sort | toward intelligent intrusion prediction for wireless sensor networks using three layer brain like learning |
| url | https://doi.org/10.1155/2012/243841 |
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