使用联合学习的物联网网络的尸网络检测和缓解模型.
Francisco Lopes de Caldas Filho1, Samuel Carlos Meneses Soares1, Elder Oroski2
1Electrical Engineering Department (ENE), Technology College, University of Brasília (UnB), Brasília 70910-900, Brazil.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
本研究引入了一种新模型,用于打击物联网 (IoT) 网络中的分布式拒绝服务 (DDoS) 攻击. 它通过使用联合学习来增强网络安全,实现近实时,准确的检测和减轻接近攻击源的攻击.
科学领域:
- 网络安全和网络基础设施
- 分布式系统和云计算云计算
- 物联网 (IoT) 安全 安全 物联网
背景情况:
- 物联网 (IoT) 设备存在重大安全漏洞,使他们成为分布式拒绝服务 (DDoS) 等网络攻击的目标.
- DDoS攻击损害了网络可用性,并通过恶意流量压倒数字基础设施而造成财务损失.
- 现有的安全措施往往难以有效地减轻来自本地网络或针对物联网生态系统的攻击.
研究的目的:
- 开发和评估一种用于缓解企业本地网络DDoS攻击的新型模型,重点是早期检测和预防.
- 通过实施分散的检测和缓解系统来增强物联网 (IoT) 环境的安全性.
- 提高网络攻击检测的准确性和效率,同时在分布式物联网基础设施中保持数据隐私.
主要方法:
- 实施主机入侵检测系统 (HIDS) 和网络入侵检测系统 (NIDS) 以全面识别异常.
- 在雾计算架构中集成主机入侵检测和预防系统 (HIDPS),以实时响应威胁.
- 使用NIDS应用联合学习,实现本地数据分析和跨物联网设备的异常流量协同检测.
主要成果:
- 拟议的模型在识别分散的物联网基础设施内的异常流量时,实现了89.753%的检测准确度.
- 分布式架构有效地阻止了体积攻击流量到达关键网络点,提高了整体系统的弹性.
- 联合学习将单个故障点的影响降至最低,并减少了单个设备上的计算工作负载,提高了系统效率和隐私.
结论:
- 开发的模型提供了一个高效和准确的解决方案,用于近实时检测和缓解物联网局部网络中的DDoS攻击.
- 联合学习与入侵检测系统相结合,为提高分散环境中的网络安全提供了强大的框架.
- 这项研究有助于加强物联网网络对恶意流量的保护,并改善数字基础设施的整体安全状况.
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