通过图形嵌入增强自主网络防御决策的新框架
Zhen Wang1,2, Yongjie Wang1,2, Xinli Xiong1,2
1College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China.
Entropy (Basel, Switzerland)
|June 26, 2025
概括
本研究引入了一种新的自主网络防御 (ACD) 方法,通过结合图形嵌入和强化学习. 该方法通过提高网络拓意识来增强决策,从而导致更有效的网络防御策略.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 传统的自主网络防御 (ACD) 方法与复杂的网络拓和相互依赖性作斗争,阻碍了关键节点和攻击路径的识别.
- 有效的ACD需要先进的决策,可以动态地适应不断变化的网络威胁和网络结构.
- 当前的方法在高维度,稀疏的网络环境中经常面临感知瓶.
研究的目的:
- 通过将图形嵌入与强化学习相结合,为自主网络防御 (ACD) 提出一种增强的决策方法.
- 改进网络拓和内部节点依赖关系的表征,以获得更明智的防御策略.
- 在高维,稀疏的网络环境中解决传统方法的局限性.
主要方法:
- 构建了网络对抗的游戏模型,以表示网络拓元素用于决策.
- 结合了Node2vec图形嵌入算法与强化学习,以增强防御者的信息表征.
- 用于节点属性和网络结构特征的低维向量嵌入,超越传统的一热编码.
- 通过新的细粒度防御机制扩展了Cyberwheel算法训练环境.
主要成果:
- 拟议的基于图形嵌入的决策方法在防御性决策的战略选择中,与传统的感知方法相比,表现优越.
- Node2vec有效地将网络属性和结构特征嵌入到低维向量中,克服了感知限制.
- 对Node2vec参数的分析证实了它们对嵌入ACD决策的有效性的影响.
结论:
- 将图形嵌入与强化学习的整合为自主网络防御决策提供了重大进展.
- 拟议的方法增强了拓意识,并使防御者能够进行动态战略优化.
- 这种方法为导航复杂和动态的网络空间环境提供了更有效和更强大的解决方案.
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