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English(EN) Autonomous Cyber Defense: Real-Time Attack Detection and Mitigation in Software-Defined Networks Using Machine Learning

机器学习系统自主防御网络免受网络攻击

一种新的机器学习系统已被开发出来,用于在软件定义网络中自主地实时检测和缓解网络攻击。该系统在最近的一篇arXiv论文中进行了详细介绍,旨在应对日益加速的网络威胁,因为攻击者可以在几分钟甚至几秒钟内横向移动。它包括两个模块:一个用于创建和预处理网络数据集,另一个用于自动化算法的训练和评估,以触发阻止操作。一个案例研究表明,该系统在21秒内成功检测并阻止了一次SYN洪水拒绝服务攻击,无需人工干预。 AI

影响 该系统可以显著缩短对网络威胁的响应时间,有可能减轻快速移动攻击造成的损害。

排序理由 该集群包含一篇在arXiv上发表的研究论文,详细介绍了一种用于网络防御的新机器学习系统。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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机器学习系统自主防御网络免受网络攻击

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该集群包含一篇在arXiv上发表的研究论文,详细介绍了一种用于网络防御的新机器学习系统。[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) · Alexandre Amaral, Fernando Moro, Ana Malheiro ·

    软件定义网络中基于机器学习的实时攻击检测与缓解:自主网络防御

    arXiv:2608.22075v2 Announce Type: replace-cross Abstract: Adversaries now move faster than manual response processes can absorb. The average eCrime breakout time, that is, the interval between initial access and the first lateral movement to another host, fell to 29 minutes in 20…