在社交媒体上使用混合深变压器模型和超参数优化优化来检测欺诈性帐户
Prashant Kumar Shukla1, Bala Dhandayuthapani Veerasamy2, Noha Alduaiji3
1Department of Computer Science and Engineering & Deputy Dean Research, Amity School of Engineering and Technology (ASET), Amity University Mumbai, Mumbai, 410206, Maharashtra, India.
Scientific reports
|November 3, 2025
概括
一个新的深度学习框架有效地检测到社交媒体的假账户. 它结合了时间卷积网络 (TCN),生成对抗网络 (GAN) 和海优化算法 (SOA) 以提高准确性和效率.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 社交媒体的增长推动了假账户的扩散,危及用户隐私和平台完整性.
- 由于不平衡,高维度和顺序的用户活动数据,检测假帐户具有挑战性.
- 现有的方法难以处理复杂的模式和风险过度匹配,需要先进的检测模型.
研究的目的:
- 提出一种新的深度学习架构,用于可扩展和精确的社交媒体欺诈检测.
- 在假账户检测中解决数据不平衡和维度挑战.
- 通过先进的技术优化模型性能和效率.
主要方法:
- 一个集成时间卷积网络 (TCN) 进行序列建模的深度学习架构.
- 基于生成对抗网络 (GAN) 的数据增强,以解决阶级不平衡.
- 自动编码器用于减小维度,海优化算法 (SOA) 用于超参数调整.
主要成果:
- 在基准数据集上,TCN-GAN-SOA框架实现了高性能 (Cresci-2017,TwiBot-22).
- 与最先进的模型相比,实现了0.96和0.95的ROC-AUC得分,具有更高的回忆精度和F1得分.
- 在处理各种欺诈行为时,证明了计算效率和稳定性.
结论:
- 拟议的框架为社交媒体欺诈检测提供了一个可扩展,可靠和准确的解决方案.
- 集成TCN,GAN和SOA提供了一种强大的方法来打击假账户.
- 这种方法提高了社交媒体平台的完整性和可信度.
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