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This paper examines how moving target defences (MTD) implemented in powersystems can be countered by unsupervised learning-based false data injection(FDI) attack and how MTD can be combined with physical watermarking to enhancethe system resilience. A novel intelligent attack, which incorporatesdensity-based spatial clustering and dimensionality reduction, is developed andshown to be effective in maintaining stealth in the presence of traditional MTDstrategies. In resisting this new type of attack, a novel implementation of MTDcombining with physical watermarking is proposed by adding Gaussian watermarkinto physical plant parameters to drive detection of traditional andintelligent FDI attacks, while remaining hidden to the attackers and limitingthe impact on system operation and stability.

Stealthy MTD Against Unsupervised Learning-based Blind FDI Attacks in Power Systems