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对物联网和WSN使用机器学习的虫洞攻击检测和缓解模型
1Department of Computer Science, College of Computer Engineering and Science, Prince Sattam bin Abdulaziz University, Alkharj, Saudi Arabia.
PeerJ. Computer science
|September 24, 2024
概括
本研究介绍了机器学习 (ML) 用于检测无线传感器网络 (WSN) 中的虫洞攻击. 支持矢量机 (SVM) 和深度神经网络 (DNN) 模型有效地识别恶意节点,提高物联网安全性.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 网络工程 网络工程
背景情况:
- 物联网 (IoT) 的扩张引入了重要的安全漏洞,特别是在无线传感器网络 (WSN) 中.
- 由于资源受限的传感器节点,WSN面临着像虫洞攻击这样的威胁.
- 有效检测这些攻击对于安全的智能基础设施至关重要.
研究的目的:
- 提出和评估机器学习 (ML) 技术,用于检测WSN中的虫洞攻击.
- 分析网络节点连接,以识别恶意活动.
- 提高物联网网络的安全性和可靠性.
主要方法:
- 在基站使用支持向量机 (SVM) 和深度神经网络 (DNN) 模型.
- 分析网络流量数据进行分类和恶意节点识别.
- 使用NS3.37模拟器和真实场景验证的模型.
主要成果:
- 拟议的ML模型在检测虫洞攻击方面表现出高效.
- 使用包括召回,假阳性率,延迟和吞吐量在内的指标来评估性能.
- 与现有方法相比,开发的方法显示出更高的性能.
结论:
- 机器学习为WSN中的虫洞攻击检测提供了一个强大的解决方案.
- 该研究强调了SVM和DNN在保护物联网环境方面的潜力.
- 这些发现有助于提高连接基础设施的整体安全性和效率.
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