对物联网网络的隐私保护方法使用统计学习与优化算法在高维的大数据环境上的优化算法.
Fatma S Alrayes1, Mohammed Maray2, Asma Alshuhail3
1Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
Scientific reports
|January 27, 2025
概括
本研究介绍了一种保护隐私的统计学习与高维大数据环境优化算法 (PPSLOA-HDBDE) 方法. 它在入侵检测中达到99.49%的准确性,增强了物联网 (IoT) 设备的数据安全性.
科学领域:
- * 计算机科学 计算机科学
- * 数据科学数据科学
- * 网络安全 网络安全
背景情况:
- * 物联网 (IoT) 设备的扩散产生了大量的高维数据,给隐私和安全带来了重大挑战.
- * 现有的保护隐私的机器学习 (ML) 解决方案通常依赖于服务器辅助,并与勾结攻击和物联网环境的动态性质作斗争.
- * 大数据环境中的高维数据使隐私保护复杂化,危及统计方法的有效性和准确性.
研究的目的:
- * 提出一种新的隐私保护统计学习与优化算法高维大数据环境 (PPSLOA-HDBDE) 方法.
- * 确保大数据场景中的数据保密性和分析效率,特别是物联网网络.
- * 解决当前服务器辅助隐私解决方案的局限性,并增强入侵检测能力.
主要方法:
- *使用线性缩放规范化 (LSN) 进行数据预处理.
- *通过基于沙猫群优化器 (SCSO) 的特征选择 (FS) 来减少尺寸.
- * 通过时间卷积网络 (TCN),多层自动编码器 (MAE) 和极端梯度增强 (XGBoost) 的集体进行入侵检测,并通过改进的海洋捕食者算法 (IMPA) 进行超参数调整.
主要成果:
- * PPSLOA-HDBDE技术在隐私保护和分析有效性方面表现出卓越的性能.
- * 在入侵检测任务中达到99.49%的高精度.
- *实验验证证了拟议方法与现有模型相比的有效性.
结论:
- * PPSLOA-HDBDE方法在高维的大数据环境中有效平衡数据隐私和分析准确性.
- * 集成先进的优化和组合技术为保护物联网数据提供了强大的解决方案.
- * 拟议的方法为大数据应用的隐私保护统计学习提供了显著的进步.
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