用轻量级深度学习模型提升大规模物联网的入侵检测能力
Deep learning based intelligent IDS for Large-scale IoT networks
- 设计CNN与LSTM两类轻量级深度学习模型用于入侵检测
- 在CICIoT2023数据集上达到超98%的分类准确率
- 适合资源受限的物联网环境部署，兼顾性能与效率
大规模物联网网络的普及既提升了自动化效率，也带来了安全风险，尤其因非法设备接入和特定攻击类型加剧了威胁。本文提出两种基于深度学习的轻量级智能入侵检测系统（IDS）：基于卷积神经网络（CNN）的IDS和基于长短期记忆网络（LSTM）的IDS。在CICIoT2023数据集上评估表明，两类模型均能有效识别并分类多种网络威胁，支持二分类、分组分类和多分类任务。其中，所提CNN-based IDS在二分类、分组分类和多分类任务中准确率分别为99.34%、99.02%和98.6%；LSTM-based IDS则分别达到99.42%、99.13%和98.68%。
原文摘要 · Abstract (English)
The proliferation of large-scale IoT networks has been both a blessing and a curse. Not only has it revolutionized the way organizations operate by increasing the efficiency of automated procedures, but it has also simplified our daily lives. However, while IoT networks have improved convenience and connectivity, they have also increased security risk due to unauthorized devices gaining access to these networks and exploiting existing weaknesses with specific attack types. The research proposes two lightweight deep learning (DL)-based intelligent intrusion detection systems (IDS). to enhance the security of IoT networks: the proposed convolutional neural network (CNN)-based IDS and the proposed long short-term memory (LSTM)-based IDS. The research evaluated the performance of both intelligent IDSs based on DL using the CICIoT2023 dataset. DL-based intelligent IDSs successfully identify and classify various cyber threats using binary, grouped, and multi-class classification. The proposed CNN-based IDS achieves an accuracy of 99.34%, 99.02% and 98.6%, while the proposed LSTM-based IDS achieves an accuracy of 99.42%, 99.13%, and 98.68% for binary, grouped, and multi-class classification, respectively.
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