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English(EN) Hybrid CNN-LSTM Framework for Intelligent Cyber Attack Detection and Prevention in U.S. Critical Digital Infrastructure: A Comparative Machine Learning Evaluation on CSE-CIC-IDS2018

AI框架提升美国关键基础设施网络安全

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个混合CNN-LSTM框架，以加强美国关键数字基础设施的网络安全。该AI驱动的系统旨在检测和预防传统方法难以识别的复杂网络威胁。该框架使用CSE-CIC-IDS2018数据集进行了评估，该数据集包含DDoS和僵尸网络等各种攻击场景，证明了识别恶意网络行为的准确性有所提高。 AI

影响 增强了关键基础设施的AI驱动威胁检测能力，提高了抵御不断演变的络攻击的韧性。

排序理由 该集群包含一篇研究论文，详细介绍了使用机器学习进行网络攻击检测的新框架。[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 →

AI框架提升美国关键基础设施网络安全

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该集群包含一篇研究论文，详细介绍了使用机器学习进行网络攻击检测的新框架。[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) · Md. Iqbal Hossan, Md. Serajul Kabir Chowdhury Rubel, Md. Arifur Rahman, B. M. Taslimul Haque ·

    用于美国关键数字基础设施智能网络攻击检测与防御的混合CNN-LSTM框架：在CSE-CIC-IDS2018上的比较机器学习评估

    arXiv:2606.05714v1 Announce Type: cross Abstract: Digital infrastructure is growing at a rapid pace in the United States, and as a result, exposure to advanced cyber threats to critical sectors including healthcare, finance, transportation, energy and government systems is growin…

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