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.
Hossein Shayeghi, Babak Mohamadi, Peyman Zare et al.· European Conference on Artif...· 0 citations
In recent years, acoustic signal analysis has emerged as a promising approach for fault detection in electromechanical systems, providing a non-invasive, low-cost, and real-time alternative to traditional monitoring techniques. This paper presents the design and implementation of a low-cost embedded system for acoustic fault detection. The proposed system is built on a Raspberry Pi Nano platform, integrating a low-power microphone, real-time audio signal preprocessing, and lightweight machine learning models for classification of normal versus faulty operating conditions. The focus is placed on developing an efficient signal processing pipeline that includes noise reduction, feature extraction (time and frequency domain descriptors, Mel-frequency cepstral coefficients), and on-device classification using compact neural network architectures. The embedded setup enables autonomous monitoring without the need for external computation resources, making it suitable for edge deployment in industrial and IoT environments. Experimental validation is carried out using publicly available datasets such as MIMII (Malfunctioning Industrial Machine Investigation and Inspection), as well as preliminary real-time recordings. The results demonstrate that the system achieves reliable fault detection accuracy while maintaining low computational and energy costs, highlighting its potential for scalable deployment in smart maintenance applications.
Andreea-Daniela Savu, N. Bizon, S. Drǎguşin· European Conference on Artif...· 0 citations