针对网络欺凌的新方法,使用基于深度学习的区块链解决方案,提供智能建议
Aliaa M Alabdali1, Arwa Mashat2
1Faculty of Computing and Information Technology, King Abdulaziz University, Department of Information Technology, Rabigh, Saudi Arabia.
Frontiers in medicine
|April 17, 2024
概括
本研究介绍了一种结合区块链和联合学习 (FL) 的新方法,用于打击网络欺凌,增强在线安全和用户隐私. 该方法利用动态贝叶斯网络 (DBN) 和长短期内存 (LSTM) 进行有效的网络欺凌检测.
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:
- 网络欺凌在社交媒体平台上是一个日益严重的问题.
- 现有的解决方案往往缺乏强大的隐私功能和有效的检测机制.
- 需要去中心化,保护隐私的AI解决方案来实现在线安全.
研究的目的:
- 通过区块链和联合学习 (FL) 引入一种用于打击网络欺凌的新方法.
- 开发一个分散的AI解决方案,优先考虑用户隐私并增强在线安全.
- 为了正式建模社会联系,并分析网络欺凌模式.
主要方法:
- 区块链技术与联邦学习 (FL) 集成,以实现分散的AI.
- 使用合金语言用于社会联系的正式建模.
- 采用长短期内存 (LSTM) 用于功能开发和动态贝叶斯网络 (DBN) 用于区块链数据的关系测试的双相方法.
主要成果:
- 拟议的方法通过新的DBN和LSTM集成,证明了有效的网络欺凌检测.
- 社交联系的正式建模为网络欺凌动态提供了洞察力.
- 使用标准指标对先前研究的性能评估表明了该方法的有效性.
结论:
- 区块链和FL的结合为分散的网络欺凌防御提供了一个变革性的愿景.
- 开发的方法增强了用户的隐私,并促进了更安全的在线环境.
- 这项研究为医疗保健和在线安全预测建模的现实应用提供了基准.
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