在物联网网络中使用深度学习和变压器进行DDoS攻击的多类入侵检测系统.
Sheikh Abdul Wahab1,2, Saira Sultana1, Noshina Tariq3
1Department of Computing and Technology, H-9 Campus, Iqra University, Islamabad 44000, Pakistan.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
本研究介绍了一种深度学习 (DL) 入侵检测系统 (IDS),用于打击物联网 (IoT) 网络中的分布式拒绝服务 (DDoS) 攻击. 基于DL的IDS有效检测各种DDoS威胁,增强物联网安全.
科学领域:
- 网络安全 网络安全
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 物联网 (IoT) 设备的扩散导致对分布式拒绝服务 (DDoS) 攻击的脆弱性增加.
- 物联网网络中的DDoS攻击会破坏通信,损害服务可用性,并造成重大运营和经济损失.
研究的目的:
- 开发和评估基于深度学习 (DL) 的入侵检测系统 (IDS),专门为物联网环境设计.
- 评估卷积神经网络 (CNN),深度神经网络 (DNN) 和变压器模型在检测物联网网络中各种类型的DDoS攻击中的有效性.
主要方法:
- 实现了三个DL架构:CNN,DNN和变压器模型用于入侵检测.
- 利用CiC IoT 2023数据集进行IDS模型的培训和测试.
- 采用数据预处理技术,包括日志规范化和基于SMOTE的过量采样,以处理特征分布和类不平衡.
主要成果:
- 在二进制分类 (DNN: 99.2%,CNN: 99.0%,变压器: 98.8%) 中实现了高精度,用于检测DDoS攻击.
- 在三类分类 (良性,DDoS,非DDoS) 中表现出近乎完美的性能 (99.9-100%).
- 在12个类别的分类中达到很高的准确性 (DNN:93.0%,CNN:92.7%,变压器:92.5%),包括良性流量和12种攻击类型.
结论:
- 拟议的基于DL的IDS显示了物联网DDoS检测的高效率,精度,回忆和ROC-AUC值.
- 该系统在检测准确性和效率方面优于现有的最先进方法,提供了可扩展的解决方案.
- 集成到IDS框架中的高级DL模型为物联网网络中不断发展的DDoS威胁提供了强大的防御.
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