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通过集体学习埃塞俄电信的案例来加强订阅欺诈的检测
Esubalew Asmare Desta1,2, Kidus Workineh Azale3,4, Abenet Alazar Hailu5,4
1Esubalew Asmare Desta: College of Informatics, University of Gondar, Gondar, Ethiopia. esu.asmare@gmail.com.
Scientific reports
|February 8, 2026
概括
本研究介绍了使用整体和自适应学习对电信订阅的先进欺诈检测. 堆叠和自适应随机森林模型被证明是识别订阅欺诈的最有效方法.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 电信 电信服务 电信服务 电信服务
背景情况:
- 订阅欺诈对全球的电信公司构成重大财务和国家安全风险.
- 埃塞俄电信在检测和减轻其网络中的订阅欺诈方面面临着严峻的挑战.
研究的目的:
- 开发和评估一个针对电信订阅欺诈的先进欺诈检测模型.
- 通过使用Ensemble和自适应学习技术来提高欺诈检测的准确性.
主要方法:
- 在预处理和特征选择后,一百万个呼叫详细记录 (CDR) 的数据集被精制为349,164个记录,保留了8个关键特征.
- 实施了个别模型 (决策树,物流回归,人工神经网络) 和整体方法 (袋装,提升,堆叠,投票).
- 包括Hoeffding树和自适应随机森林在内的自适应模型被用于欺诈检测.
主要成果:
- 功能选择确定了有效检测欺诈的8个关键特性.
- 集成方法,特别是堆叠,在识别欺诈活动方面表现出强的表现.
- 适应性随机森林 (ARF) 在订阅欺诈检测方面也表现出强大的能力.
结论:
- 建议将堆叠和自适应随机森林 (ARF) 作为在电信中检测订阅欺诈的高效模型.
- 开发的模型为埃塞俄电信等公司在打击订阅欺诈方面取得了重大进展.
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