SAFE-CAST:基于集群的自动监控和机器对机器通信信任的安全AI联合计数
Yusuf Kursat Tuncel1, Kasım Öztoprak1
1Department of Computer Engineering, Konya Food And Agriculture University, Konya, Turkey.
PeerJ. Computer science
|February 3, 2025
概括
SAFE-CAST提高了机器对机器 (M2M) 通信的物联网 (IoT) 安全性和效率. 这一框架显著提高了能源效率,吞吐量和安全性,同时减少了延迟和数据包丢失.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 物联网 (IoT) 中的机器对机器 (M2M) 通信面临着重大的安全和效率挑战.
- 现有的解决方案往往无法平衡强大的安全性和能源效率,特别是在资源有限的环境中.
研究的目的:
- 提出SAFE-CAST,这是一种用于基于集群的自动化监控和信任的安全AI联合计数的新型框架.
- 解决物联网M2M通信中的关键安全性和效率问题.
主要方法:
- 使用联合学习与劳埃德的K-意味着安全的集群.
- 采用质量多样性优化算法 (QDOA) 进行安全的频道选择.
- 集成了一个基于区块链的动态信任管理系统.
- 包含适应性多代理强化学习以实现上下文感知传输 (AMARLCAT),以最大限度地减少延迟并提高可扩展性.
主要成果:
- 在模拟中,SAFE-CAST显示了与现有方法相比的显著改进.
- 在能源消耗方面实现了21.6%的降低.
- 增加了14.5%的吞吐量和15.3%的安全强度.
- 降低了33.9%的延迟时间和12.9%的数据包丢失率.
结论:
- SAFE-CAST为物联网和边缘计算中的强大,高效和安全的M2M通信提供了全面的解决方案.
- 该框架有效地平衡了复杂网络环境中的安全性和性能要求.
关键词:
区块链信任管理 区块链信任管理边缘计算安全 边缘计算安全联合学习是联合学习.混合注意力机制 混合注意力机制物联网安全安全 互联网安全机器与机器之间的通信.多个代理强化学习学习的多个代理强化学习.量子衍生优化的优化根据SAFE-CAST的框架.安全的聚类安全的聚类.更多相关视频
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