在Instagram上使用集体学习方法检测假个人资料
Bharti Goyal1, Nasib Singh Gill1, Preeti Gulia1
1Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak, Haryana, India.
Scientific reports
|July 21, 2025
概括
这项研究引入了一种机器学习模型来检测假冒的Instagram帐户,大大提高了在线安全和用户信任. 先进的混合系统实现了高精度,有效地减少垃圾邮件和欺骗性内容.
科学领域:
- 计算机科学 计算机科学
- 社交媒体安全 社交媒体安全
- 机器学习 机器学习
背景情况:
- 在Instagram上的假账户有助于垃圾邮件,有害信息和欺骗性内容,侵蚀用户的信任并危及在线安全.
- 现有的识别虚假个人资料的方法不足,需要先进的解决方案来保持平台的完整性.
研究的目的:
- 开发和评估一个高度准确的机器学习模型,用于识别假冒的Instagram帐户.
- 提高在线身份验证系统的有效性和可信度.
主要方法:
- 实现一个混合系统,将XGBoost,SMOTE用于类平衡,以及GridSearchCV用于超参数调.
- 使用scale_pos_weight优化和适应性发现用于假冒账户的趋势分析.
- 利用随机森林超参数微调来提高检测准确度.
主要成果:
- 拟议模型的F1得分为98%,回忆率为98%,精度为98.3%,准确率为98.24%.
- 显著减少了假账户,提高了平台的安全性.
- 建立了在社交媒体环境中保护信任的新标准.
结论:
- 开发的混合机器学习系统提供了一种最先进的解决方案,用于检测Instagram上的假账户.
- 这项研究改善了在线身份验证系统,并加强了用户的信任.
- 这些发现为未来在社交媒体安全和打击欺骗性在线实践方面的进展奠定了基础.
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