利用储能设施防御对抗攻击
Defending Against Adversarial Attacks by Energy Storage Facility
摘要：
针对数据驱动算法的对抗攻击将成为电网安全的一种新型威胁。文献表明，对深度神经网络的对抗攻击会严重误导电力系统的负荷预测。然而，目前还不清楚这种新型攻击如何影响电网系统的运行。在这项研究中，我们证明了对抗性算法攻击会导致显著的成本增加风险，而间歇性可再生能源的日益普及将加剧这种风险。在德克萨斯州，5%的对抗攻击可以在一个季度内使总发电成本增加17%，约占2000万美元。当风能穿透率增加到40%以上时，5%的对抗攻击将使发电成本增加23%。我们的研究发现了一种防御对抗攻击的新方法：投资于储能系统。目前所有的文献都集中在开发防御对抗攻击的算法上。我们是第一个研究揭示了在物理系统中使用该设施来防御物联网系统（如智能电网系统）中的对抗算法攻击的能力。
英文摘要：
Adversarial attacks on data-driven algorithms applied in the power system will be a new type of threat to grid security. Literature has demonstrated that the adversarial attack on the deep-neural network can significantly mislead the load fore-cast of a power system. However, it is unclear how the new type of attack impacts the operation of the grid system. In this research, we manifest that the adversarial algorithm attack induces a significant cost-increase risk which will be exacerbated by the growing penetration of intermittent renewable energy. In Texas, a 5% adversarial attack can increase the total generation cost by 17% in a quarter, which accounts for around $20 million. When wind-energy penetration increases to over 40%, the 5% adversarial attack will inflate the genera-tion cost by 23%. Our research discovers a novel approach to defending against the adversarial attack: investing in the energy-storage system. All current literature focuses on developing algorithms to defend against adversarial attacks. We are the first research revealing the capability of using the facility in a physical system to defend against the adversarial algorithm attack in a system of the Internet of Things, such as a smart grid system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
