Telecommunication Network Fraud Early Warning Based on Blockchain and Federated Learning

DOI: https://doi.org/10.62517/jsse.202608105

Author(s)

Haoliang Lan, Zhikang Xiang

Affiliation(s)

Department of Computer Information and Network Security, Jiangsu Police Institute, Nanjing, China

Abstract

In response to the challenges of passive case analysis and disposal, insufficient utilization of data value mining, and low degree of collaborative sharing of intelligence and information technology in the crackdown and governance of telecommunication network fraud crimes, the application of blockchain combined with federated learning in telecommunication network fraud early warning aims to achieve automatic analysis and disposal of fraud cases, collaborative sharing and analysis of intelligence information, and value fusion mining of multi-source data. The paper based on blockchain and federated learning, a "end-chain-end" decentralized layered distributed architecture and related functional modules were designed, and the data processing, model training, chain collaboration, and chain application involved in the functional modules were elaborated in detail. At the same time, a specific analysis was conducted on the advantages of the system by combining security, efficiency, and persistence. The various functional modules operate independently and collaborate closely, which helps to achieve data security sharing, collaborative modeling, and accurate early warning under the participation of multiple institutions, providing a new theoretical and application paradigm for telecommunication network fraud early warning.

Keywords

Telecommunication Network Fraud; Early Warning; Blockchain; Federated Learning; Hierarchical Distributed Architecture

References

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