消除假冒:基于边缘信心评估的社交机器人检测
概括
本研究介绍了一种基于边缘信心评估 (BECE) 的机器人检测方法,以提高在线社交网络的安全性. BECE有效地识别了不可靠的连接,提高了图形神经网络 (GNN) 在检测复杂的社交机器人的性能.
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:
- 在线社交网络 (OSN) 面临来自复杂社交机器人的威胁.
- 图形神经网络 (GNN) 是对机器人检测有希望的,但与不可靠的交互边缘作斗争.
- 先进的机器人通过与真实帐户交互来伪装,创建杂的图形结构.
研究的目的:
- 提出一种新的社交机器人检测方法,基于边缘信心评估 (BECE) 的机器人检测方法.
- 通过评估边缘信心来提高基于GNN的机器人检测的可靠性.
- 改善在 OSN 图表中机器人和人类表示之间的差异化.
主要方法:
- 开发了一个边缘信心评估模块来评估和识别不可靠的边缘.
- 基于用户节点表示的设计边缘功能.
- 使用参数化的高斯分布用于边缘嵌入和Kullback-Leibler (KL) 分歧进行优化.
主要成果:
- 在三个真实世界数据集中,BECE在社交机器人检测方面表现出卓越的有效性.
- 边缘信任评估模块在与六个广泛使用的GNN架构集成时提高了检测性能.
- 该方法成功地减轻了基于GNN的机器人检测中不可靠边缘的影响.
结论:
- BECE提供了一个强大的解决方案,用于在OSN中检测社交机器人.
- 边缘信心评估模块是一个多功能插件,用于增强现有的GNN模型.
- 这种方法显著提高了在线社交平台的完整性和安全性.
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