Detection of selective forwarding attacks based on adaptive learning automata and communication quality in wireless sensor networks
Wireless sensor networks face threats of selective forwarding attacks which are simple to implement but difficult to detect. It is difficult to distinguish between malicious packet dropping and the normal packet loss on unstable wireless channels. For this situation, a selective forwarding attack de...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-11-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147718815046 |
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| _version_ | 1832547329288175616 |
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| author | Hongliang Zhu Zhihua Zhang Juan Du Shoushan Luo Yang Xin |
| author_facet | Hongliang Zhu Zhihua Zhang Juan Du Shoushan Luo Yang Xin |
| author_sort | Hongliang Zhu |
| collection | DOAJ |
| description | Wireless sensor networks face threats of selective forwarding attacks which are simple to implement but difficult to detect. It is difficult to distinguish between malicious packet dropping and the normal packet loss on unstable wireless channels. For this situation, a selective forwarding attack detection method is proposed based on adaptive learning automata and communication quality; the method can eliminate the impact of normal packet loss on selective forwarding attack detection and can detect ordinary selective forwarding attack and special cases of selective forwarding attack. The current and comprehensive communication quality of nodes are employed to reflect the short- and long-term forwarding behaviors of nodes, and the normal packet loss caused by unstable channels and medium-access-control layer collisions is considered. The adaptive reward and penalty parameters of a detection learning automata are determined by the comprehensive communication quality of the node and the voting of its neighbors to reward normal nodes or punish malicious ones. Simulation results indicate the effectiveness of the proposed method in detecting ordinary selective forwarding attacks, black-hole attacks, on-off attacks, and energy exhaustion attacks. In addition, the communication overhead of the method is lower than that of other methods. |
| format | Article |
| id | doaj-art-ef944b472a5b40889e01afe8d4549c55 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2018-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-ef944b472a5b40889e01afe8d4549c552025-02-03T06:45:17ZengWileyInternational Journal of Distributed Sensor Networks1550-14772018-11-011410.1177/1550147718815046Detection of selective forwarding attacks based on adaptive learning automata and communication quality in wireless sensor networksHongliang Zhu0Zhihua Zhang1Juan Du2Shoushan Luo3Yang Xin4Beijing University of Posts and Telecommunications, Beijing, ChinaInformation Science Academy, China Electronics Technology Group Corporation, Beijing, ChinaBeijing University of Posts and Telecommunications, Beijing, ChinaBeijing University of Posts and Telecommunications, Beijing, ChinaBeijing University of Posts and Telecommunications, Beijing, ChinaWireless sensor networks face threats of selective forwarding attacks which are simple to implement but difficult to detect. It is difficult to distinguish between malicious packet dropping and the normal packet loss on unstable wireless channels. For this situation, a selective forwarding attack detection method is proposed based on adaptive learning automata and communication quality; the method can eliminate the impact of normal packet loss on selective forwarding attack detection and can detect ordinary selective forwarding attack and special cases of selective forwarding attack. The current and comprehensive communication quality of nodes are employed to reflect the short- and long-term forwarding behaviors of nodes, and the normal packet loss caused by unstable channels and medium-access-control layer collisions is considered. The adaptive reward and penalty parameters of a detection learning automata are determined by the comprehensive communication quality of the node and the voting of its neighbors to reward normal nodes or punish malicious ones. Simulation results indicate the effectiveness of the proposed method in detecting ordinary selective forwarding attacks, black-hole attacks, on-off attacks, and energy exhaustion attacks. In addition, the communication overhead of the method is lower than that of other methods.https://doi.org/10.1177/1550147718815046 |
| spellingShingle | Hongliang Zhu Zhihua Zhang Juan Du Shoushan Luo Yang Xin Detection of selective forwarding attacks based on adaptive learning automata and communication quality in wireless sensor networks International Journal of Distributed Sensor Networks |
| title | Detection of selective forwarding attacks based on adaptive learning automata and communication quality in wireless sensor networks |
| title_full | Detection of selective forwarding attacks based on adaptive learning automata and communication quality in wireless sensor networks |
| title_fullStr | Detection of selective forwarding attacks based on adaptive learning automata and communication quality in wireless sensor networks |
| title_full_unstemmed | Detection of selective forwarding attacks based on adaptive learning automata and communication quality in wireless sensor networks |
| title_short | Detection of selective forwarding attacks based on adaptive learning automata and communication quality in wireless sensor networks |
| title_sort | detection of selective forwarding attacks based on adaptive learning automata and communication quality in wireless sensor networks |
| url | https://doi.org/10.1177/1550147718815046 |
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