The rapid deployment of intelligent electronic devices and the development of information and communication technology have improved distribution network (DN) reliability with feeder automation. Thus, the operation of DNs has become more dependent on the cyber-physical components. As the cyber security of smart grid has drawn increasing attention in recent years, this paper proposes a simple yet powerful type of attack targeting the remote terminal units. The proposed attack policy can manipulate the feeder automation operation with the aim of cutting off important power consumers. Regarding the realistic cyber-attacks that previously occurred, an optimal attack model is established for analyzing the attack policy. This optimization model aims to minimize the attack cost and the penalty of being caught or detected while maximizing the remuneration. A Bayesian attack graph model is adopted to quantify the successful probability of exploiting known and zero-day vulnerabilities. The probability of a cyber-attack being caught or detected is modeled based on search theory. Next, an enhanced self-adaptive evolutionary programming is developed to achieve satisfactory solutions for practical applications. Finally, the proposed model and corresponding solution strategies are verified using the RBTS bus 2 DN and the modified IEEE 123-node test feeder.

中文翻译：

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馈线自动化系统的发起人激励性攻击计划

智能电子设备的快速部署以及信息和通信技术的发展通过馈线自动化提高了配电网（DN）的可靠性。因此，DN的操作已变得越来越依赖于网络物理组件。随着近年来智能电网的网络安全越来越受到关注，本文提出了一种针对远程终端单元的简单而强大的攻击类型。提出的攻击策略可以操纵馈线自动化操作，以切断重要的用电设备。针对先前发生的实际网络攻击，建立了用于分析攻击策略的最佳攻击模型。该优化模型旨在最大程度地降低攻击成本和被捕获或检测到的惩罚，同时使报酬最大化。采用贝叶斯攻击图模型来量化利用已知和零日漏洞的成功概率。根据搜索理论对捕获或检测到网络攻击的可能性进行建模。接下来，开发了增强的自适应进化编程，以实现实际应用中令人满意的解决方案。最后，使用RBTS总线2 DN和改进的IEEE 123节点测试馈线验证了所提出的模型和相应的解决方案策略。