基于PSO-DT的BagDT:一个强大的轻量级组合框架,用于在物联网环境中高效的功能选择和DDoS攻击检测
1School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, 632014, India.
Scientific reports
|October 16, 2025
概括
一个新的基于PSO-DT的BagDT组合模型有效地检测物联网 (IoT) 中的分布式拒绝服务 (DDoS) 攻击. 这种轻量级模型实现了高精度,使其成为资源有限的智能环境的理想选择.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
背景情况:
- 物联网 (IoT) 已经大幅扩展,增加了对网络威胁的脆弱性,例如分布式拒绝服务 (DDoS) 攻击.
- 实时检测DDoS攻击对于保护物联网环境和防止关键服务中断至关重要.
- 现有的深度学习模型 (CNN,LSTM) 对于资源有限的物联网设备来说,通常过于计算密集.
研究的目的:
- 为物联网环境中实时DDoS攻击检测提出一个强大高效的混合框架.
- 为解决物联网深度学习模型中高计算开销的局限性.
- 开发适合当代智能环境的轻量级和可扩展的解决方案.
主要方法:
- 开发了一种使用粒子优化 (PSO) 结合决策树 (DT) 的混合框架,以有效选择特征.
- 评估了PSO-DT特征选择算法与集体学习者:随机子空间KNN,AdaBoost,RUSBoost和袋装决策树 (BagDT).
- 专注于降低计算成本和模型大小,同时保持高检测精度.
主要成果:
- 拟议的基于PSO-DT的BagDT组合模型实现了99.96%的准确性,宏观平均精度,回忆和F1得分为0.99.
- 与其他变体相比,BagDT模型显示精度增加了4.13%,训练时间减少了95.49%.
- 整体吞吐量增加了63.52%,证实了该模型的效率.
结论:
- 基于PSO-DT的BagDT组合模型为物联网中的实时DDoS攻击检测提供了优越,高效和可扩展的解决方案.
- 混合方法有效地减少了复杂性和计算开销,使其适合资源有限的物联网设备.
- 该模型的高性能验证了其在现代智能环境中实施的潜力.
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