在物联网环境中检测攻击的新型混合深度学习模型:采用变压器方法的卷积神经网络
1Computer Science (FPA), Dada Lakhmi Chand State University of Performing and Visual Arts (DLCSUPVA); garg04muskan@gmail.com.
Journal of visualized experiments : JoVE
|December 8, 2025
概括
本研究介绍了物联网 (IoT) 网络的深度学习安全方法. 混合CNN-变压器模型有效地检测和分类物联网威胁,具有高准确性和效率.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 物联网 (IoT) 设备的普及增加了网络连接和可访问性.
- 这种增强的互连性引入了新的安全漏洞和威胁水平.
- 强大的异常检测系统对于保护现代物联网环境至关重要.
研究的目的:
- 介绍一种基于深度学习的新型安全方法,用于减轻物联网 (IoT) 网络中的威胁.
- 开发和评估混合卷积神经网络 (CNN) 和变压器模型,用于实时攻击检测和分类.
- 在准确性,效率和计算复杂性方面评估模型的性能.
主要方法:
- 一个混合CNN-Transformer深度学习模型被设计用于分析网络流量模式.
- 该模型是使用全面的CIC-IoT-2023数据集进行训练和验证的,该数据集包括33种不同的物联网威胁类型.
- 性能指标包括精度,回忆,F1得分,准确性和损失都被仔细评估.
主要成果:
- 拟议的混合CNN-变压器模型实现了特殊的性能指标:99.96%的精度,99.96%的回忆,99.96%的F1得分和99.97%的准确性.
- 该模型显示了0.0123的低损失,表明高度有效的学习和预测.
- 分析证实了最小化的计算复杂性和资源消耗,同时保持了卓越的攻击检测能力.
结论:
- 开发的深度学习方法为保护物联网网络免受复杂的网络威胁提供了高效和高效的解决方案.
- 混合CNN-变压器模型为复杂的物联网生态系统中的实时异常检测和分类提供了一个强大的框架.
- 这项研究在物联网安全方面取得了重大进展,将高精度与计算效率相平衡.
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