一个双层优化策略,用于功能选择在强大的对抗性攻击缓解物联网上的网络安全
Kashi Sai Prasad1, P Udayakumar2, E Laxmi Lydia3
1Department of CSE-AI&ML, MLR Institute of Technology, Hyderabad, India.
Scientific reports
|January 17, 2025
概括
这项研究引入了一个新的模型,用于在物联网网络安全中强大的对抗性攻击缓解. TTOS-RAAM 模型有效地检测出 99.91% 的准确度的对抗性攻击,增强物联网数据保护.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
背景情况:
- 敌对攻击对网络安全构成越来越大的威胁,特别是在互联物联网系统的背景下.
- 虽然深度学习 (DL) 模型被用于入侵检测系统 (IDS),但它们对对抗性示例的脆弱性仍然是一个开放的研究领域.
- 在工业4.0中对物联网,人工智能和5G的越来越多的依赖加剧了由于大量数据处理的安全问题.
研究的目的:
- 为增强物联网网络安全引入一种新的强有力的敌对攻击缓解 (TTOS-RAAM) 模型的双层优化策略.
- 为了应对在物联网环境中检测对抗性攻击行为的挑战.
- 评估DL模型在物联网网络中的对抗性攻击方面的有效性.
主要方法:
- 数据预处理使用最小-最大缩放器进行统一的输入.
- 最佳特征选择采用了科阿蒂-灰狼优化 (CGWO) 的混合体.
- 使用条件变异自编码器 (CVAE) 检测敌对攻击,通过改进的混沌非洲优化 (ICAVO) 进行参数调整.
主要成果:
- TTOS-RAAM模型在检测对抗性攻击方面表现出卓越的性能.
- 对RT-IoT2022数据集的实验分析显示,高精度为99.91%.
- 拟议的方法在物联网网络的对抗性攻击缓解方面显著优于现有的方法.
结论:
- TTOS-RAAM模型提供了一个强大的解决方案,用于减轻物联网网络安全中的对抗性攻击.
- 该研究强调了高级优化和DL技术在保护物联网数据方面的潜力.
- 这些发现有助于正在进行的关于保护智能系统免受复杂的网络威胁的研究.
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