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深度学习的有效性与基于狐优化器的特征选择模型,以确保物联网环境中的网络攻击检测
1Department of Computer Science, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia. mkhonaini@psu.edu.sa.
Scientific reports
|August 6, 2025
概括
本研究介绍了一种基于福克斯优化器的特征选择与深度学习以确保网络攻击检测 (FOFSDL-SCD) 模型,以增强物联网 (IoT) 网络安全. 这种新的方法显著提高了智能城市网络中威胁检测准确性和弹性.
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:
- 智能城市利用物联网 (IoT) 设备提高效率和可持续性.
- 物联网在智能城市的普及引入了关键的网络安全挑战,包括数据泄露和未经授权的访问.
- 现有的网络安全措施需要先进的技术来有效保护相互连接的物联网系统.
研究的目的:
- 开发和评估一种用于增强物联网网络网络安全的新型模型.
- 提高智能城市基础设施对抗网络攻击的弹性和威胁检测能力.
- 为智能网络安全解决方案分析先进的深度学习技术.
主要方法:
- 提出了一种基于Fox优化器的特征选择与深度学习以确保网络攻击检测 (FOFSDL-SCD) 模型.
- 数据预处理涉及最小至最大的规范化;特征选择使用了狐优化算法 (FOA).
- 使用时间卷积网络 (TCN) 进行分类,超参数通过灰虫优化 (DBO) 进行优化.
主要成果:
- 在Edge-IIoT数据集上,FOFSDL-SCD模型实现了卓越的性能.
- 该模型显示了高精度 (99.38%),精度 (96.27%),回忆 (96.26%) 和F1-Score (96.27%).
- 对比分析证实,FOFSDL-SCD方法的表现优于现有的网络安全模型.
结论:
- FOFSDL-SCD模型为在物联网环境中检测网络攻击提供了强大而有效的解决方案.
- 福克斯优化器和深度学习的整合大大提高了物联网网络安全性.
- 这项研究有助于推进智慧城市的智能网络安全战略.
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