Distributed Classification of Localization Attacks in Sensor Networks Using Exchange-Based Feature Extraction and Classifier
Secure localization under different forms of attack has become an essential task in wireless sensor networks. Despite the significant research efforts in detecting the malicious nodes, the problem of localization attack type recognition has not yet been well addressed. Motivated by this concern, we...
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Online Access: | http://dx.doi.org/10.1155/2016/8672305 |
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doaj-39f74a54587a4f088cc9beb7e92f66a12020-11-24T23:49:23ZengHindawi LimitedJournal of Sensors1687-725X1687-72682016-01-01201610.1155/2016/86723058672305Distributed Classification of Localization Attacks in Sensor Networks Using Exchange-Based Feature Extraction and ClassifierSu-Zhe Wang0Yong Li1Wei Cheng2School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, ChinaSecure localization under different forms of attack has become an essential task in wireless sensor networks. Despite the significant research efforts in detecting the malicious nodes, the problem of localization attack type recognition has not yet been well addressed. Motivated by this concern, we propose a novel exchange-based attack classification algorithm. This is achieved by a distributed expectation maximization extractor integrated with the PECPR-MKSVM classifier. First, the mixed distribution features based on the probabilistic modeling are extracted using a distributed expectation maximization algorithm. After feature extraction, by introducing the theory from support vector machine, an extensive contractive Peaceman-Rachford splitting method is derived to build the distributed classifier that diffuses the iteration calculation among neighbor sensors. To verify the efficiency of the distributed recognition scheme, four groups of experiments were carried out under various conditions. The average success rate of the proposed classification algorithm obtained in the presented experiments for external attacks is excellent and has achieved about 93.9% in some cases. These testing results demonstrate that the proposed algorithm can produce much greater recognition rate, and it can be also more robust and efficient even in the presence of excessive malicious scenario.http://dx.doi.org/10.1155/2016/8672305 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Su-Zhe Wang Yong Li Wei Cheng |
spellingShingle |
Su-Zhe Wang Yong Li Wei Cheng Distributed Classification of Localization Attacks in Sensor Networks Using Exchange-Based Feature Extraction and Classifier Journal of Sensors |
author_facet |
Su-Zhe Wang Yong Li Wei Cheng |
author_sort |
Su-Zhe Wang |
title |
Distributed Classification of Localization Attacks in Sensor Networks Using Exchange-Based Feature Extraction and Classifier |
title_short |
Distributed Classification of Localization Attacks in Sensor Networks Using Exchange-Based Feature Extraction and Classifier |
title_full |
Distributed Classification of Localization Attacks in Sensor Networks Using Exchange-Based Feature Extraction and Classifier |
title_fullStr |
Distributed Classification of Localization Attacks in Sensor Networks Using Exchange-Based Feature Extraction and Classifier |
title_full_unstemmed |
Distributed Classification of Localization Attacks in Sensor Networks Using Exchange-Based Feature Extraction and Classifier |
title_sort |
distributed classification of localization attacks in sensor networks using exchange-based feature extraction and classifier |
publisher |
Hindawi Limited |
series |
Journal of Sensors |
issn |
1687-725X 1687-7268 |
publishDate |
2016-01-01 |
description |
Secure localization under different forms of attack has become an essential task in wireless sensor networks. Despite the significant research efforts in detecting the malicious nodes, the problem of localization attack type recognition has not yet been well addressed. Motivated by this concern, we propose a novel exchange-based attack classification algorithm. This is achieved by a distributed expectation maximization extractor integrated with the PECPR-MKSVM classifier. First, the mixed distribution features based on the probabilistic modeling are extracted using a distributed expectation maximization algorithm. After feature extraction, by introducing the theory from support vector machine, an extensive contractive Peaceman-Rachford splitting method is derived to build the distributed classifier that diffuses the iteration calculation among neighbor sensors. To verify the efficiency of the distributed recognition scheme, four groups of experiments were carried out under various conditions. The average success rate of the proposed classification algorithm obtained in the presented experiments for external attacks is excellent and has achieved about 93.9% in some cases. These testing results demonstrate that the proposed algorithm can produce much greater recognition rate, and it can be also more robust and efficient even in the presence of excessive malicious scenario. |
url |
http://dx.doi.org/10.1155/2016/8672305 |
work_keys_str_mv |
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