基于深度强化学习的多阶段信息物理协同拓扑攻击方法
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TM732

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国家自然科学基金资助项目(51774088)执行起止时间:2018年01月01日-2021年12月31日;基金题目:电场作用下表面活性剂在多孔介质中传质动力学特性研究;作者徐建军为国家自然科学基金的申请人;目前暂未有其他基金项目。


A multi-stage coordinated cyber-physical topology attack method based on deep reinforcement learning
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    摘要:

    随着智能电网的发展及通信设备不断引入到信息物理系统(cyber physical system,CPS)中,CPS正面临一种破坏性更强的新型攻击方式——信息物理协同攻击(coordinated cyber physical attack,CCPA),其隐蔽性与威胁性易导致系统出现级联故障。首先,基于攻击者的视角,提出一种多阶段信息物理协同拓扑攻击模型,单阶段的物理攻击使线路中断,双阶段的网络攻击分别用来掩盖物理攻击的断开线路和制造一条新的虚假断开线路。其次,结合深度强化学习(deep reinforcement learning,DRL)理论,提出一种基于深度Q网络(deep Q-network,DQN)的最小攻击资源确定方法。然后,给出攻击者考虑上层最大化物理攻击效果和下层最小化攻击代价的具体模型及求解方法。最后,以IEEE 30节点系统为例,验证了所提多阶段攻击模型的有效性。仿真结果表明,多阶段信息物理协同拓扑攻击较单一攻击更加隐蔽且有效,对电网的破坏程度更大,为防御此类攻击提供了参考。

    Abstract:

    With the development of smart grid and the continuous introduction of communication equipments into cyber physical system (CPS),CPS is confronted with a new attack mode with more destructive—coordinated cyber physical attack (CCPA). CCPA is not only hidden but also threatening,which is easy to cause cascading failures. Firstly,from the perspective of the attacker,a multi-stage coordinated cyber-physical topology attack model is proposed. The single-stage physical attack first trips a transmission line,and the two-stage cyber attack is used to mask the outage signal of the disconnected line in the physical layer and then create a new fake tripped line in the cyber layer. Secondly,combined with deep reinforcement learning (DRL) theory,the method for determining the minimum attack resources based on deep Q-network (DQN) is proposed. Then,the specific model and solution method for the attacker are given,taking the maximization of the physical attack effect in the upper layer and minimization of the attack cost in the lower layer into consideration. Finally,the IEEE 30-bus system is taken as an example to verify the effectiveness of the proposed multi-stage attack model. The simulation results demonstrate that the multi-stage coordinated cyber-physical topology attack is more hidden and effective than the single attack,and the damage to the power grid is greater,which provides a reference for the defender against such attacks.

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伊娜,徐建军,陈月,孙迪康.基于深度强化学习的多阶段信息物理协同拓扑攻击方法[J].电力工程技术,2023,42(4):149-158

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历史
  • 收稿日期:2023-01-18
  • 最后修改日期:2023-03-09
  • 录用日期:2023-03-10
  • 在线发布日期: 2023-07-20
  • 出版日期: 2023-07-28