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Graph theoretical defense mechanisms against false data injection attacks in smart grids

机译:图论防御智能电网中错误数据注入攻击的理论防御机制

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This paper addresses false data injection, which is one of the most significant security challenges in smart grids. Having an accurately estimated state is of great importance for maintaining a stable running condition of smart grids. To preserve the accuracy of the estimated state, bad data detection (BDD) mechanisms are utilized to remove erroneous measurements due to meter failures or outsider attacks. In this paper we use a graph-theoretical formulation for false data injection attacks in smart grids and propose defense mechanisms to mitigating this type of attacks. To this end we discuss characteristics of a typical smart grid graph such as planarity. Then we propose three different approaches for finding optimal protected meters set: a fast and efficient heuristic algorithm that works well in practice, an approximation algorithm that provides guarantee for the quality of the protected set, and an exact algorithm that find the optimal solution. Our extensive simulation results show that our algorithms outperform similar existing solutions in terms of different performance metrics.
机译:本文解决了错误的数据注入问题,这是智能电网中最重大的安全挑战之一。具有准确估计的状态对于维持智能电网的稳定运行状态至关重要。为了保持估计状态的准确性,不良数据检测(BDD)机制用于消除由于仪表故障或外部攻击而导致的错误测量。在本文中,我们对智能电网中的虚假数据注入攻击使用了图论公式,并提出了缓解这种攻击的防御机制。为此,我们讨论了典型智能网格图的特性,例如平面度。然后,我们提出了三种找到最佳受保护仪表集的不同方法:一种在实践中运行良好的快速高效的启发式算法;一种为受保护集的质量提供保证的近似算法;以及一种寻找最优解的精确算法。我们广泛的仿真结果表明,就不同的性能指标而言,我们的算法优于类似的现有解决方案。

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