Ensuring security in edge computing through effective blockchain node detection

Abstract The rapid development of blockchain technology has garnered increasing attention, particularly in the field of edge computing. It has become a significant subject of research in this area due to its ability to protect the privacy of data. Despite the advantages that blockchain technology of...

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Main Authors: Shenqiang Wang, Zhaowei Liu, Haiyang Wang, Jianping Wang
Format: Article
Language:English
Published: SpringerOpen 2023-06-01
Series:Journal of Cloud Computing: Advances, Systems and Applications
Subjects:
Online Access:https://doi.org/10.1186/s13677-023-00466-y
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author Shenqiang Wang
Zhaowei Liu
Haiyang Wang
Jianping Wang
author_facet Shenqiang Wang
Zhaowei Liu
Haiyang Wang
Jianping Wang
author_sort Shenqiang Wang
collection DOAJ
description Abstract The rapid development of blockchain technology has garnered increasing attention, particularly in the field of edge computing. It has become a significant subject of research in this area due to its ability to protect the privacy of data. Despite the advantages that blockchain technology offers, there are also security threats that must be addressed. Attackers may manipulate certain nodes in the blockchain network, which can result in tampering with transaction records or other malicious activities. Moreover, the creation of a large number of false nodes can be utilized to gain control and manipulate transaction records of the blockchain network, which can compromise the reliability and security of edge computing. This paper proposes a blockchain node detection method named $$T^2A2vec$$ T 2 A 2 v e c that provides a more secure, credible, and reliable solution to address these challenges. In order to achieve $$T^2A2vec$$ T 2 A 2 v e c , a transaction dataset that is evenly distributed in both space and time was collected. The transaction dataset is constructed as a transaction graph, where nodes represent accounts and edges describe transactions. BP neural network is used to extract account features, and a random walk strategy based on transaction time, type, and amount is used to extract transaction features. The obtained account features and transaction features are fused to obtain account representation. Finally, the obtained node representation is fed into different classifiers to identify malicious nodes.
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spelling doaj.art-ddfd8a1d17954b95b409ebb08c99607d2023-06-18T11:24:54ZengSpringerOpenJournal of Cloud Computing: Advances, Systems and Applications2192-113X2023-06-0112111610.1186/s13677-023-00466-yEnsuring security in edge computing through effective blockchain node detectionShenqiang Wang0Zhaowei Liu1Haiyang Wang2Jianping Wang3School of Computer Science and Control Engineering, Yantai UniversitySchool of Computer Science and Control Engineering, Yantai UniversityInstitute of Network Technology YantaiShandong marine resources and environment Research InstituteAbstract The rapid development of blockchain technology has garnered increasing attention, particularly in the field of edge computing. It has become a significant subject of research in this area due to its ability to protect the privacy of data. Despite the advantages that blockchain technology offers, there are also security threats that must be addressed. Attackers may manipulate certain nodes in the blockchain network, which can result in tampering with transaction records or other malicious activities. Moreover, the creation of a large number of false nodes can be utilized to gain control and manipulate transaction records of the blockchain network, which can compromise the reliability and security of edge computing. This paper proposes a blockchain node detection method named $$T^2A2vec$$ T 2 A 2 v e c that provides a more secure, credible, and reliable solution to address these challenges. In order to achieve $$T^2A2vec$$ T 2 A 2 v e c , a transaction dataset that is evenly distributed in both space and time was collected. The transaction dataset is constructed as a transaction graph, where nodes represent accounts and edges describe transactions. BP neural network is used to extract account features, and a random walk strategy based on transaction time, type, and amount is used to extract transaction features. The obtained account features and transaction features are fused to obtain account representation. Finally, the obtained node representation is fed into different classifiers to identify malicious nodes.https://doi.org/10.1186/s13677-023-00466-yBlockchainGraph EmbeddingSecurityEdge Computing
spellingShingle Shenqiang Wang
Zhaowei Liu
Haiyang Wang
Jianping Wang
Ensuring security in edge computing through effective blockchain node detection
Journal of Cloud Computing: Advances, Systems and Applications
Blockchain
Graph Embedding
Security
Edge Computing
title Ensuring security in edge computing through effective blockchain node detection
title_full Ensuring security in edge computing through effective blockchain node detection
title_fullStr Ensuring security in edge computing through effective blockchain node detection
title_full_unstemmed Ensuring security in edge computing through effective blockchain node detection
title_short Ensuring security in edge computing through effective blockchain node detection
title_sort ensuring security in edge computing through effective blockchain node detection
topic Blockchain
Graph Embedding
Security
Edge Computing
url https://doi.org/10.1186/s13677-023-00466-y
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