Data Fusion Approach for Collaborative Anomaly Intrusion Detection in Blockchain-Based Systems
Blockchain technology is rapidly changing the transaction behavior and efficiency of businesses in recent years. Data privacy and system reliability are critical issues that is highly required to be addressed in Blockchain environments. However, anomaly intrusion poses a significant threat to a Bloc...
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| Veröffentlicht in: | IEEE internet of things journal 2022-08, Vol.9 (16), p.14741-14751 |
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| creator | Liang, Wei Xiao, Lijun Zhang, Ke Tang, Mingdong He, Dacheng Li, Kuan-Ching |
| description | Blockchain technology is rapidly changing the transaction behavior and efficiency of businesses in recent years. Data privacy and system reliability are critical issues that is highly required to be addressed in Blockchain environments. However, anomaly intrusion poses a significant threat to a Blockchain, and therefore, it is proposed in this article a collaborative clustering-characteristic-based data fusion approach for intrusion detection in a Blockchain-based system, where a mathematical model of data fusion is designed and an AI model is used to train and analyze data clusters in Blockchain networks. The abnormal characteristics in a Blockchain data set are identified, a weighted combination is carried out, and the weighted coefficients among several nodes are obtained after multiple rounds of mutual competition among clustering nodes. When the weighted coefficient and a similarity matching relationship follow a standard pattern, an abnormal intrusion behavior is accurately and collaboratively detected. Experimental results show that the proposed algorithm has high recognition accuracy and promising performance in the real-time detection of attacks in a Blockchain. |
| doi_str_mv | 10.1109/JIOT.2021.3053842 |
| format | Article |
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Data privacy and system reliability are critical issues that is highly required to be addressed in Blockchain environments. However, anomaly intrusion poses a significant threat to a Blockchain, and therefore, it is proposed in this article a collaborative clustering-characteristic-based data fusion approach for intrusion detection in a Blockchain-based system, where a mathematical model of data fusion is designed and an AI model is used to train and analyze data clusters in Blockchain networks. The abnormal characteristics in a Blockchain data set are identified, a weighted combination is carried out, and the weighted coefficients among several nodes are obtained after multiple rounds of mutual competition among clustering nodes. When the weighted coefficient and a similarity matching relationship follow a standard pattern, an abnormal intrusion behavior is accurately and collaboratively detected. 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| subjects | Algorithms Analytical models Anomaly detection Blockchain Cluster analysis Clustering Clustering algorithms Collaboration Cryptography data fusion Data integration Data models Intrusion Intrusion detection Mathematical models Nodes Security similarity matching System reliability weighted combination |
| title | Data Fusion Approach for Collaborative Anomaly Intrusion Detection in Blockchain-Based Systems |
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