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普及既带来了益处也带来了挑战。它不仅通过提升自动化流程的效率革新了组织的运营方式，也简化了我们的日常生活。然而，尽管物联网网络提升了便利性与连接性，但也因未经授权的设备接入网络并利用特定攻击类型钻探现有漏洞而增加了安全风险。本研究提出了两种基于轻量级深度学习（DL）的智能入侵检测系统（IDS）以增强物联网网络的安全性：所提出的基于卷积神经网络（CNN）的IDS和基于长短期记忆（LSTM）的IDS。研究使用CICIoT2023数据集评估了这两种基于DL的智能IDS的性能。基于DL的智能IDS通过二元、分组及多类别分类，成功识别并分类了多种网络威胁。所提出的基于CNN的IDS在二元、分组及多类别分类中分别达到了99.34%、99.02%和98.6%的准确率，而所提出的基于LSTM的IDS则分别达到了99.42%、99.13%和98.68%的准确率。