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.

翻译：大规模物联网网络的激增既是福音也是诅咒。它不仅通过提升自动化流程的效率彻底改变了组织的运作方式，还简化了我们的日常生活。然而，物联网网络在提升便利性和连接性的同时，也因未授权设备接入网络并利用特定攻击类型利用现有弱点而增加了安全风险。本研究提出两种轻量级深度学习智能入侵检测系统，以增强物联网网络的安全性：所提出的基于卷积神经网络的入侵检测系统和基于长短期记忆网络的入侵检测系统。研究使用CICIoT2023数据集评估了这两种基于深度学习的智能入侵检测系统的性能。基于深度学习的智能入侵检测系统通过二分类、分组分类和多分类成功识别并分类各种网络威胁。所提出的基于CNN的入侵检测系统在二分类、分组分类和多分类中分别达到99.34%、99.02%和98.6%的准确率，而所提出的基于LSTM的入侵检测系统则分别达到99.42%、99.13%和98.68%的准确率。