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English(EN) Dueling Deep Q-Learning for Intrusion Detection

双Q学习模型在入侵检测中达到99.68%的准确率

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种利用双Q学习模型的新型入侵检测系统（IDS），准确率达到99.68%。该模型采用双网络架构，将价值流和优势流分开，提高了学习效率和稳定性。该系统在CIC-IDS2018数据集上进行了训练，该数据集包含DDoS和僵尸网络等各种攻击类型。此外，还集成了可解释AI（XAI）技术，特别是SHAP，以提供模型预测的可解释性。 AI

影响 这项研究引入了一种更有效、更具可解释性的网络威胁检测方法，有可能提高安全系统抵御不断变化的攻击向量的鲁棒性。

排序理由 该集群包含一篇详细介绍用于入侵检测的新型机器学习方法的学术论文。[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 →

双Q学习模型在入侵检测中达到99.68%的准确率

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该集群包含一篇详细介绍用于入侵检测的新型机器学习方法的学术论文。[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) · Logan Luna (Georgia Institute of Technology), Matthew P. Berkowitz (Embry-Riddle Aeronautical University), Laxima Niure Kandel (Embry-Riddle Aeronautical University), Sirio Jansen-S'anchez (Embry-Riddle Aeronautical University) ·

    双重深度Q学习用于入侵检测

    arXiv:2608.11291v1 Announce Type: cross Abstract: Intrusion detection systems (IDS) and automated systems for detecting and reporting cyber threats, are commonly handled via supervised machine learning methods. Though effective, these models struggle to effectively adapt to new a…

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