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English(EN) A Hybrid CNN-LSTM Intrusion Detection Framework for Cybersecurity in Smart Renewable Energy Grids

混合CNN-LSTM模型提升可再生能源电网网络安全

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的混合CNN-LSTM框架，旨在增强智能可再生能源电网的网络安全。该模型通过结合CNN的空间特征提取和LSTM的时间序列建模，能有效检测即时异常和渐进式、低速攻击活动。该框架在NSL-KDD等基准数据集上展示了高精度和高召回率，精度高达98.2%，并且其效率通过GPU上每秒27,800个流的实时推理吞吐量得到证实，表明其在资源受限设备上部署的可行性。 AI

影响 通过改进对可再生能源电网中复杂网络攻击的检测能力，增强了关键基础设施的安全性。

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

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

混合CNN-LSTM模型提升可再生能源电网网络安全

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详细介绍特定应用新模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Sajib Debnath, Remon Das ·

    面向智能可再生能源电网网络安全的混合CNN-LSTM入侵检测框架

    arXiv:2606.25200v1 Announce Type: new Abstract: The accelerated digitalization of renewable energy smart grids through IoT sensors, AMI, and SCADA systems has significantly expanded the attack surface for sophisticated cyberattacks, FDI attacks that stealthily distort state estim…

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