A Literature Review on Spark-Based Optimization of Machine Learning Algorithms for Cyber Fraud Detection

Junyi Zou

doi:10.47297/taposatWSP2633-456925.20250604

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A Literature Review on Spark-Based Optimization of Machine Learning Algorithms for Cyber Fraud Detection

Volume 6, Issue 4

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A Literature Review on Spark-Based Optimization of Machine Learning Algorithms for Cyber Fraud Detection
A Literature Review on Spark-Based Optimization of Machine Learning Algorithms for Cyber Fraud Detection
Ludwig-Maximilians-Universität(LMU), Munich, Bayern, 80539, Germany
Vol. 6, Issue 4, Pages: 144-148(2025)
Published：22 April 2025
DOI：10.47297/taposatWSP2633-456925.20250604
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Zou Junyi.A Literature Review on Spark-Based Optimization of Machine Learning Algorithms for Cyber Fraud Detection[J].Theory and Practice of Science and Technology,2025,06(04):144-148.
DOI：

Zou Junyi.A Literature Review on Spark-Based Optimization of Machine Learning Algorithms for Cyber Fraud Detection[J].Theory and Practice of Science and Technology,2025,06(04):144-148. DOI： 10.47297/taposatWSP2633-456925.20250604.

摘要

Abstract

With the explosive growth of internet data and the diversification of cyber fraud techniques

traditional machine learning algorithms face challenges such as low efficiency

poor real-time performance

and insufficient dynamic adaptability when processing large-scale fraudulent information. Addressing the economic losses and social harm caused by cyber fraud requires new high-efficiency technical solutions. The Spark framework

leveraging its distributed computing capabilities

in-memory acceleration

and machine learning libraries

offers a novel technical pathway for optimizing fraud detection. This paper systematically reviews recent research progress in Spark-based machine learning algorithms for cyber fraud information processing

focusing on the real-time data processing capabilities of the Spark platform

optimization of distributed algorithms

and innovative models tailored to fraud characteristics. It reveals that existing studies have achieved significant algorithm acceleration and improved classification performance. However

limitations persist in multi-source data adaptability

dynamic fraud pattern recognition

and cross-scenario generalization. Future research should integrate deep learning with real-time stream processing to develop more robust fraud detection frameworks. This paper provides a technical pathway reference for optimizing cyber fraud information processing

offering both theoretical significance and practical value for enhancing the real-time responsiveness and precision of cybersecurity defenses.

关键词

Keywords

references

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