用改进的CNN-LSTM模型提升物联网网络入侵检测准确率
An Improved CNN-LSTM Based Intrusion Detection System for IoT Networks
- 结合卷积与循环神经网络，同时捕捉流量的空间和时序特征
- 在物联网数据上实现约97%的检测准确率，稳定识别多种攻击类型
- 适合需要高精度实时防护的物联网安全场景
随着物联网设备的快速普及，网络安全问题急剧上升，入侵检测系统成为保护网络环境的关键。本文提出一种改进的基于CNN-LSTM的入侵检测模型，融合多类别分类、数据集整合与时序特征学习，以提升物联网网络中的检测性能。利用网络流量数据，在入侵检测任务上进行评估，模型达到约97%的准确率。实验结果表明，该模型能有效检测多种攻击类别，同时保持训练与验证性能的稳定性。卷积与循环神经网络组件的结合使框架能够捕捉网络流量的时空特性，显著提升了物联网环境下的整体入侵检测能力。
原文摘要 · Abstract (English)
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.
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