Smart grids leverage the data collected from smart meters to make important operational decisions. However, they are vulnerable to False Data Injection (FDI) attacks in which an attacker manipulates meter data to disrupt the grid operations. Existing works on FDI are based on a simple threat model in which a single grid operator has access to all the data, and only some meters can be compromised. Our goal is to secure smart grids against FDI under a realistic threat model. To this end, we present a threat model in which there are multiple operators, each with a partial view of the grid, and each can be fully compromised. An effective defense against FDI in this setting is to share data between the operators. However, the main challenge here is to incentivize data sharing. We address this by proposing an incentive mechanism that rewards operators for uploading data, but penalizes them if the data is missing or anomalous. We derive formal conditions under which our incentive mechanism is provably secure against operators who withhold or distort measurement data for profit. We then implement the data sharing solution on a private blockchain, introducing several optimizations that overcome the inherent performance limitations of the blockchain. Finally, we conduct an experimental evaluation that demonstrates that our implementation has practical performance.
翻译:智能电网利用从智能米收集的数据来做出重要的操作决定。然而,它们很容易受到虚假数据输入攻击,攻击者在攻击中操纵数据以干扰电网业务。关于外国直接投资的现有工作基于简单的威胁模式,即单一电网经营者能够获取所有数据,只能损害几米。我们的目标是在现实的威胁模式下确保智能电网,以对抗外国直接投资。为此,我们提出了一个威胁模式,即有多个操作者,每个操作者都有部分的网格,每个操作者都可以完全受损。在这一背景下,对外国直接投资的有效防范是让操作者共享数据。然而,这里的主要挑战是激励数据共享。我们提出奖励机制,奖励操作者上载数据,但如果数据缺失或异常,则惩罚他们。我们得出正式条件,根据这些条件,我们的激励机制对拒绝或扭曲测量数据以盈利为目的的操作者具有可辨别的安全性。我们随后在私人电链上实施数据共享解决方案,引入了几种最优化的方法,以克服障碍的内在性能限制。我们通过实验性评估来证明我们的执行情况。