Ensuring security in edge computing through effective blockchain node detection

• Published in: Journal of cloud computing : advances, systems and applications Vol. 12; no. 1; pp. 88 - 16
• Main Authors: Wang, Shenqiang, Liu, Zhaowei, Wang, Haiyang, Wang, Jianping
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2023; Springer Nature B.V; SpringerOpen
• Subjects: Access control; Back propagation networks; Blockchain; Cloud computing; Computer Communication Networks; Computer Science; Computer System Implementation; Computer Systems Organization and Communication Networks; Cryptography; Cybersecurity; Data integrity; Datasets; Edge Computing; Graph Embedding; Graph theory; Information Systems Applications (incl.Internet); Internet of Things; Methods; Neural networks; Nodes; Privacy; Random walk; Representations; Security; Security and privacy issues for artificial intelligence in edge-cloud computing; Software Engineering/Programming and Operating Systems; Special Purpose and Application-Based Systems; Edge Computing; Graph Embedding; Security; Blockchain
• ISSN: 2192-113X; 2192-113X
• DOI: 10.1186/s13677-023-00466-y

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 2 A 2 v e c that provides a more secure, credible, and reliable solution to address these challenges. In order to achieve 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.