知识转移 基于DRL的对手对网络攻击的积极分布网络 伏尔特-瓦尔控制代理:何时以及如何
IEEE transactions on cybernetics
|February 16, 2026
概括
针对主动配电网络 (ADN) 中的电压变频控制 (VVC) 深度强化学习 (DRL) 的网络攻击可能会导致电压违规. 这项研究介绍了一种新的DRL对手,它可以发动定时和隐蔽的攻击,绕过检测机制.
科学领域:
- 电气工程 电气工程
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 基于深度增强学习 (DRL) 的活跃分发网络 (ADN) 的电压变频控制 (VVC) 易受敌对网络攻击的影响.
- 现有的攻击往往忽略了坏数据检测 (BDD) 机制,并假定攻击者资源是无限的,从而限制了它们在现实世界中的适用性.
研究的目的:
- 开发一种基于DRL的新型对手,能够在ADN中对VVC特工发动战略定时和隐蔽的攻击.
- 通过优化攻击时间和确保对BDD机制的隐形性来最大限度地提高DNA中的电压违规.
主要方法:
- 将对抗性攻击重新制定为混合动力国家-对抗性马尔科夫决策过程 (MDP).
- 设计一个新的目标功能,以最大限度地侵犯电压,最小的攻击,并纳入攻击时间.
- 在攻击向量上实施扰动集转换,绕过BDD并确保隐形.
主要成果:
- 拟议的DRL对手成功地生成了战略定时和隐蔽的对手样本.
- 这些样本显著影响了ADN中VVC剂的电压调节能力.
- 这些攻击证明了绕过BDD机制的有效性,超过了基线方法.
结论:
- 开发的基于DRL的对手提供了一个强大的方法来评估DNA中基于DRL的VVC的安全性.
- 战略定时和隐身对于对抗VVC系统的有效攻击至关重要.
- 这项研究强调了需要加强BDD机制,以抵御复杂的对抗性攻击.
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