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

6卷第4期

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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
2025年6卷第4期页码：144-148
纸质出版日期：2025-04-22
DOI：10.47297/taposatWSP2633-456925.20250604
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(2025). A Literature Review on Spark-Based Optimization of Machine Learning Algorithms for Cyber Fraud Detection. 科技理论与实践（英文版）, 6(4), 144-148.

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.
(2025). A Literature Review on Spark-Based Optimization of Machine Learning Algorithms for Cyber Fraud Detection. 科技理论与实践（英文版）, 6(4), 144-148. DOI： 10.47297/taposatWSP2633-456925.20250604.

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

Tao Huang and Liting Gao. "Real-Time Data Collection and Processing Based on Spark." Journal of Hebei University of Architecture and Civil Engineering, vol. 40 , no. 04 , 2022, pp. 176–179+188.

Lei Zhao, Ji'an Xia, Yang Wu, et al. "Performance Comparison of Classification Algorithms Based on the Spark Platform." Computer and Digital Engineering, vol. 52 , no. 03 , 2024, pp. 688–691+704.

Shuyan Jia. "Construction of a Big Data Intelligent Analysis Platform Based on Spark Technology." Journal of Binzhou University, vol. 39 , no. 06 , 2023, pp. 86 – 91 . DOI:10.13486/j.cnki.1673-2618.2023.06.012.

Yongjun Bai. "Research on Network Anomaly Traffic Data Capture Methods Based on Big Data Technology." Cybersecurity and Informatization, no. 07 , 2024, pp. 37 – 39 .

Murat Aydogan and Ali Karci. "Spam Mail Detection Using Naive Bayes Method with Apache Spark." 2018.

İskender Ülgen Oğul, Caner Ozcan, and Özlem Hakdağlı. "Fast Text Classification with Naive Bayes Method on Apache Spark." 2017.

A. N. M. JayaLakshmi and K. V. Krishna Kishore. "Performance Evaluation of DNN with Other Machine Learning Techniques in a Cluster Using Apache Spark and MLlib." 2022.

Jiacheng Wang. "Research on Spark-Based Network Intrusion Detection Algorithms." Master's thesis, Nanchang University, 2023. DOI:10.27232/d.cnki.gnchu.2023.001559.

Shihao Lü. "Design and Implementation of a Spark-Based Network Traffic Anomaly Detection System." Master's thesis, Beijing Jiaotong University, 2023. DOI:10.26944/d.cnki.gbfju.2023.001326.

Jiaqi Yao. "Research on Spark-Based Network Traffic Classification Technology." Master's thesis, North University of China, 2023. DOI:10.27470/d.cnki.ghbgc.2023.000755.

Yang Hu, Xuegang Hu, and Peipei Li, et al. "A Distributed Fast Short Text Data Stream Classification Method Based on Spark." Computer Engineering and Applications, vol. 56 , no. 14 , 2020, pp. 138 – 147 .

Xianchun Zhou, Heng Xiao, and Pingping Jiao, et al. "Malware Maximal Frequent Subgraph Mining Method Based on Spark Platform." Modern Computer, vol. 29 , no. 14 , 2023, pp. 57 – 61 .

Yazhou Li. "Research on Software Vulnerability Automatic Classification Method Based on NLP Neural Networks." PhD diss., Yanshan University, 2020. DOI:10.27440/d.cnki.gysdu.2020.001926.

Deniz Kılınç. "A Spark-Based Big Data Analysis Framework for Real-Time Sentiment Prediction on Streaming Data." 2019.

Rafał Kozik. "Distributing Extreme Learning Machines with Apache Spark for NetFlow-Based Malware Activity Detection." 2018.

Raed A. Hasan, Royida A. Ibrahem Alhayali, Nashwan Dheyaa Zaki, and Ahmed Hussien Ali. "An Adaptive Clustering and Classification Algorithm for Twitter Data Streaming in Apache Spark." 2019.

Andronicus A. Akinyelu. "Advances in Spam Detection for Email Spam, Web Spam, Social Network Spam, and Review Spam: ML-Based and Nature-Inspired-Based Techniques." 2021.

Michael Crawford, Taghi M. Khoshgoftaar, Joseph D. Prusa, Aaron N. Richter, and Hamzah Al Najada. "Survey of Review Spam Detection Using Machine Learning Techniques." 2015.

Naw Safrin Sattar, Shaikh Arifuzzaman, Minhaz F. Zibran, and Md Mohiuddin Sakib. "Detecting Web Spam in Webgraphs with Predictive Model Analysis." 2019.

Shodh Sagar and Balachandar Paulraj. "Leveraging Machine Learning for Improved Spam Detection in Online Networks." Universal Research Reports, 2024.

Pinnapureddy Manasa, Arun Malik, Khaled N. Alqahtani, et al. "Tweet Spam Detection Using Machine Learning and Swarm Optimization Techniques." IEEE Transactions on Computational Social Systems, vol. 11 , 2024, pp. 4870 – 4877 .

Anikait Kapoor. "SMS Spam Detection Using Machine Learning Approach." International Journal of Research in Science and Technology, 2024.

Caixu Wu and Jing Zhang. "Simulation of Potential Anomaly Identification in Big Data Terminals Based on Spark Computing." Software, vol. 45 , no. 06 , 2024, pp. 31 – 33 .

Ning Wang. "Research on Distributed Image Segmentation Methods Using the Spark Platform." PhD diss., University of Chinese Academy of Sciences (Aerospace Information Research Institute), 2022. DOI:10.44231/d.cnki.gktxc.2022.000072.

Muhammad Salman, Muhammad Ikram, and M. Kâafar. "Investigating Evasive Techniques in SMS Spam Filtering: A Comparative Analysis of Machine Learning Models." IEEE Access, vol. 12 , 2024, pp. 24306 – 24324 .

Konstantinos I. Roumeliotis, Nikolaos D. Tselikas, and Dimitrios K. Nasiopoulos. "Next-Generation Spam Filtering: Comparative Fine-Tuning of LLMs, NLPs, and CNN Models for Email Spam Classification." Electronics, 2024.

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