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STID-Net:优化物联网中的入侵检测,使用渐变下降
James Deva Koresh Hezekiah1, Usha Nandini Duraisamy2, Kalaichelvi Nallusamy3
1Department of Electronics and Communication Engineering, Centre for IoT and AI (CITI), KPR Institute of Engineering and Technology, Coimbatore 641 407, Tamil Nadu, India.
Sensors (Basel, Switzerland)
|April 28, 2025
概括
本研究介绍了STID-Net,这是一个用于物联网 (IoT) 环境的先进入侵检测系统. 在医疗和工业环境中,STID-Net有效地识别了复杂的网络威胁,高精度超过现有方法.
科学领域:
- 网络安全 网络安全
- 网络安全 网络安全
- 机器学习 机器学习
背景情况:
- 医疗和工业领域物联网 (IoT) 设备的扩散扩大了网络漏洞.
- 现有的入侵检测系统 (IDS) 通常无法捕捉动态物联网数据中的复杂,不规则的模式,从而限制了它们的适用性.
- 一个强大且可扩展的IDS对于保护各种物联网应用程序至关重要.
研究的目的:
- 提出STID-Net,一种新的入侵检测系统,旨在解决动态物联网环境中当前方法的局限性.
- 增强网络入侵数据中空间和时间模式的检测,包括长期依赖关系.
- 评估STID-Net在不同物联网应用数据集中的性能和稳定性.
主要方法:
- STID-Net集成了定制的卷积内核用于空间特征提取和长短期内存 (LSTM) 层用于时间序列建模.
- 整合了注意力机制,以改善入侵模式中长期依赖的检测.
- 该系统在医疗物联网 (IoMT) 和工业物联网 (IIoT) 数据集上使用小批量梯度下降 (MBGD) 和随机梯度下降 (SGD) 优化器进行了实验.
主要成果:
- STID-Net实现了高精度,SGD优化在IoMT上产生98.58%,在IIoT数据集上产生99.15%,超过MBGD优化 (分别为97.14%和97.85%).
- 该SGD优化器显示了更快的收和更好的权重调整,证明对杂的数据集有效.
- STID-Net的性能优于独立的卷积神经网络 (CNN) 和LSTM模型,展示了其卓越的性能和稳定性.
结论:
- 在动态入侵数据中,STID-Net在识别不规则模式和长期依赖性方面表现出卓越的能力.
- 拟议的模型对于各种物联网应用,特别是在医疗和工业领域,是强大的和可扩展的.
- SGD优化提高了STID-Net的性能,使其成为应对现实世界的网络安全挑战的可靠解决方案.
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