Proactive AI Enhanced Consensus Algorithm with Fraud Detection in Blockchain

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dc.contributor.authorDas, Vinamra
dc.contributor.authorCherukuri, Aswani Kumar
dc.contributor.authorHu, Qin
dc.contributor.authorKamalov, Firuz
dc.contributor.authorJonnalagadda, Annapurna
dc.date.accessioned2023-10-08T16:10:13Z
dc.date.available2023-10-08T16:10:13Z
dc.date.copyright© 2023
dc.date.issued2023
dc.description.abstractThe security and transparency provided to the data in blockchain are unmatchable, with the least instances of system hack or failure reported. With a number of consensus algorithms used in the past and the presence of leader nodes in many of them, it is important to check the leader node’s activities. As the system is large, the usage of artificial intelligence and deep learning methodologies seems the right choice to monitor the leader node’s activities. Hence in this chapter, an algorithm is proposed as to how should the consensus algorithm be modified while adding deep learning techniques to keep track of the leader node’s selection behaviors. It also explains how the system detects and moves back to stability once such a scenario is encountered. Hence in this work, the artificial neural network is used to learn the node selection behavior of the leader node by taking in 5 input parameters: sender ID, receiver ID, transaction amount, sender’s balance, and receiver’s balance. Output is either 0 (not selected into the chain) or 1 (selected into the chain) once trained neurons (each input parameter) are tested for it’s sensitivity to the selection. If it exceeds a threshold value, it is assumed to be biased upon that parameter/s, and further consensus occurs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
dc.identifier.citationDas, V., Cherukuri, A.K., Hu, Q., Kamalov, F., Jonnalagadda, A. (2023). Proactive AI Enhanced Consensus Algorithm with Fraud Detection in Blockchain. In Y. Maleh, M. Alazab & I. Romdhani. (Eds.) Blockchain for Cybersecurity in Cyber-Physical Systems. Advances in Information Security, 102, (pp. 259 - 274). Springer, Cham. https://doi.org/10.1007/978-3-031-25506-9_13
dc.identifier.issn15682633
dc.identifier.urihttps://doi.org/10.1007/978-3-031-25506-9_13
dc.identifier.urihttps://hdl.handle.net/20.500.12519/877
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofseriesBlockchain for Cybersecurity in Cyber-Physical Systems. Advances in Information Security; Volume 102
dc.rightsLicense to reuse abstract has been secured and provided by Springer Nature and Copyright Clearance Center.
dc.rights.holderCopyright : © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
dc.subjectArtificial intelligence
dc.subjectBlockchain
dc.subjectConsensus algorithm
dc.subjectCyber-physical systems
dc.subjectDeep learning
dc.subjectLeader node
dc.subjectMimic selection behavior
dc.subjectSelection pattern
dc.titleProactive AI Enhanced Consensus Algorithm with Fraud Detection in Blockchain
dc.typeBook chapter

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