Diversifying Moving Target Defense Against Adaptive Attacks Through Factorized Diffusion

Proactive defense offers a means to enhance power-system cybersecurity through controlled modifications of system configurations. One example is moving target defense (MTD), which uses distributed flexible AC transmission system (D-FACTS) devices to perturb transmission-line reactances against stealthy false data injection attacks. Existing MTD methods primarily rely on deterministic optimization, producing a single perturbation plan for each operating condition. However, repeated or highly correlated plans may expose predictable system configurations that attackers can learn and exploit. To address this limitation, this paper reformulates MTD as a conditional generative problem that produces diverse, secure, and physically feasible perturbation candidates. Learning such a distribution is challenging because MTD decisions jointly involve selecting branches, determining perturbation magnitudes, and coordinating adjustments across the network. To make this problem tractable, we factorize the decision distribution into three sequential conditional stages: branch selection, magnitude allocation, and continuous refinement. Graph-conditioned diffusion models are employed to capture the discrete and continuous decisions while accounting for network dependencies. Furthermore, a - objective is incorporated into diffusion training to favor candidates with desirable defense effectiveness and operational costs. Numerical studies on the IEEE 118- and 300-bus systems show that the proposed framework achieves AC feasibility comparable to that of optimization-based MTD baselines and higher - values, while reducing online computation time by an order of magnitude. The generated candidate pool remains effective under changing operating conditions and device availability, providing diverse high-value alternatives for flexible online cyber defense.