English(EN) An Improved CNN-LSTM Based Intrusion Detection System for IoT Networks

CNN-LSTM模型将物联网入侵检测准确率提升至97%

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-05 04:00

研究人员开发了一种改进的物联网网络入侵检测系统，采用了CNN-LSTM模型。该系统集成了多类别分类和时间特征学习，以提高检测准确性，在网络流量数据上达到了约97%。该模型的架构能有效捕捉网络流量的空间和时间特征，从而提高了物联网环境下的入侵检测能力。 AI

影响通过改进威胁检测来增强日益增长的物联网设备的安全性。

排序理由详细介绍特定应用新模型架构的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Mohammad Tariq Ikhlas, Pohanyar Khowaja Khil, Malik Muhammad Mueed Aslam, Muhammad Khuram Shahzad · 2026-06-05 04:00

一种改进的基于CNN-LSTM的物联网网络入侵检测系统

arXiv:2606.05776v1 Announce Type: cross Abstract: 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 …