Digital-Intelligence Deep-Learning for Confidential-Computing under Zero-Trust Tokenization Against Cyber Attacks

Abstract

Cyber-physical energy systems rely on digital measurements, state estimation, and learning-assisted monitoring, yet these layers are vulnerable to stealthy false data injection attacks that can distort operator awareness without triggering residual alarms. This paper presents a physics-constrained deep generative framework for attack synthesis in power-system state estimation. Conditional generative adversarial networks, autoencoders, and variational autoencoders are compared under a unified physics-aware setting that embeds the nonlinear measurement function, local state-estimation sensitivity, residual preservation, and reconstruction consistency. The framework evaluates generated attacks on IEEE 14-bus, 57-bus, and 118-bus systems using convergence behavior, bad data detection bypass rate, and Jensen-Shannon divergence. Results show that the variational autoencoder and autoencoder achieve stronger residual evasion, whereas the conditional generative adversarial network yields more realistic measurement distributions. Confidential computing and zero-trust tokenization are incorporated as interpretive security layers for protected measurement handling, tokenized provenance, and joint residual distributional trust assessment within state-estimation security decision workflows.