Osprey优化算法与图形神经网络集成,用于无线传感器网络的入侵检测
Divya Bhavani Mohan1, Prakash Arumugam2, Anand R3,4
1Unitedworld Institute of Technology, Karnavati University, Gandhinagar, Gujarat, India. divyamohan2009@gmail.com.
Scientific reports
|December 29, 2025
概括
一个新的Osprey优化算法和图形神经网络 (OOA-GNN) 模型增强了无线传感器网络的安全性. 这种OOA-GNN方法显著提高了入侵检测的准确性,并减少了WSN中的错误报警.
科学领域:
- 网络安全 网络安全
- 网络安全 网络安全
- 机器学习 机器学习
背景情况:
- 无线传感器网络 (WSN) 越来越容易受到复杂的网络攻击.
- 传统的入侵检测系统 (IDS) 具有有限的能力,高错误报警率和复杂的数据处理.
- 需要先进的IDS来确保WSN的安全性.
研究的目的:
- 提出一种新的OOA-GNN模型,以提高WSN中的入侵检测.
- 为了提高攻击检测的准确性和效率,同时最大限度地减少错误警报.
- 利用图形结构的深度学习来识别WSN数据中的复杂关系.
主要方法:
- 开发了一个新的OOA-GNN框架,集成了 Osprey优化算法 (OOA) 和图形神经网络 (GNN).
- 利用深度学习框架对WSN数据的图形表示来捕捉复杂的网络模式.
- 应用合成少数群体过量采样技术 (SMOTE) 来解决无线传感器网络数据集 (WSN-DS) 中的数据不平衡.
- 微调的GNN超参数使用OOA来优化检测性能.
主要成果:
- 在不平衡的WSN-DS数据集上,OOA-GNN模型实现了99.68%的高精度.
- OOA-GNN的表现优于包括AdaBoost,GBM,XGBoost,KNN-AOA和KNN-PSO在内的传统分类器.
- 与传统方法相比,在低假阳性率和适应网络波动的能力方面表现出优异的性能.
结论:
- 通过高效准确的入侵检测,OOA-GNN模型为WSN安全提供了显著的进步.
- 将OOA的参数调整与GNN的基于图形的框架集成,可以增强实时WSN操作.
- 拟议的方法有效地减少了错误报警,并提高了整体网络可靠性和攻击检测精度.
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