IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
Abstract
This paper examines how moving target defenses
(MTD) implemented in power systems can be countered
by unsupervised learning-based false data injection (FDI) attack
and how MTD can be combined with physical watermarking
to enhance the system resilience. A novel intelligent attack,
which incorporates dimensionality reduction and density-based
spatial clustering, is developed and shown to be effective in
maintaining stealth in the presence of traditional MTD strategies.
In resisting this new type of attack, a novel implementation
of MTD combining with physical watermarking is proposed by
adding Gaussian watermark into physical plant parameters to
drive detection of traditional and intelligent FDI attacks, while
remaining hidden to the attackers and limiting the impact on
system operation and stability.
