With the rapid proliferation of IoT devices, security concerns have dramatically escalated and intrusion detection systems have become critical for protecting networked environments. This paper presents an improved CNN-LSTM based intrusion detection model that combines multi-class classification, dataset integration, and temporal feature learning to enhance detection performance in IoT networks. Using network traffic data, the proposed approach is evaluated on intrusion detection tasks and achieves an accuracy of approximately 97%. Experimental results demonstrate that the model effectively detects multiple attack categories while maintaining stable training and validation performance. The integration of convolutional and recurrent neural network components enables the framework to capture both spatial and temporal characteristics of network traffic, improving overall intrusion detection capability in IoT environments.

翻译：随着物联网设备数量的快速激增，安全威胁急剧增加，入侵检测系统已成为保护网络环境的关键。本文提出了一种基于改进CNN-LSTM的入侵检测模型，该模型结合了多类别分类、数据集整合和时序特征学习，以提升物联网网络的检测性能。采用网络流量数据，所提方法在入侵检测任务上进行了评估，达到了约97%的准确率。实验结果表明，该模型能够有效检测多种攻击类别，同时保持稳定的训练和验证性能。通过集成卷积神经网络与循环神经网络组件，该框架能够捕获网络流量的空间与时间特征，从而提升物联网环境下的整体入侵检测能力。