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The shift of electrical power systems toward intelligent smart-grid architectures delivers better dependability, operational performance, and real-time visibility, yet simultaneously heightens exposure to cyber threats. Thanks to the massive amounts of data now generated in smart grids, machine learning techniques have emerged as a particularly promising avenue for bolstering defensive capabilities. While the strengths of machine-learning solutions for cybersecurity improvement are clear, these same models form part of the attack surface and remain especially susceptible to carefully crafted adversarial perturbations. This work analyzes the resilience of autoencoder-based intrusion detection mechanisms in smart grids against adversarial examples. We begin by presenting an original iterative procedure for synthesizing adversarial instances. Next, we demonstrate that a white-box adversary with full knowledge of the autoencoder detector can reliably produce stealthy, misclassifying samples using the proposed approach. Experimental results show that simply starting from basic adversarial seeds is insufficient to generate effective evasions, underscoring the considerable difficulty of constructing potent adversarial examples targeting autoencoder-based anomaly detectors deployed in smart-grid environments.

To effectively respond to ongoing global shifts—including globalization, climate change, and population expansion—contemporary urban areas need to integrate digital technologies into their governance and operations. This study explores a framework for urban digitalization, drawing inspiration from the digital twin initiative implemented in Rotterdam. It outlines data-handling approaches suitable for diverse data origins. The discussion also covers associated cybersecurity vulnerabilities and the possible exploitation of smart city infrastructure for military purposes. Finally, by examining publicly accessible datasets on air quality monitoring and electricity use in public lighting, the study compares the functional capabilities of Rotterdam’s digital twin with those of the digital platform developed for Brno. The analysis reveals that each municipality adopts a distinct digital transformation strategy, making a standardized, universal model impractical. Consequently, urban digitalization remains highly context-specific and individualized. At present, both platforms primarily enable the visualization of aggregated urban data. However, neither has yet incorporated sophisticated capabilities, such as artificial intelligence-supported decision-making or predictive modeling of urban phenomena. Nevertheless, they generate valuable datasets that hold significant potential for external stakeholders and third-party applications.

This paper presents a real-time preventive control approach utilizing deep reinforcement learning (DRL) to identify optimal control actions that maintain an adequate secure operating limit (SOL) in power systems. The SOL represents the boundary of the most stressed pre-contingency operating condition that the system can endure following a specified set of credible contingencies while satisfying predefined stability requirements. The proposed DRL technique employs a hybrid control framework that enables simultaneous management of both discrete and continuous control variables. Its effectiveness is assessed using a modified Nordic32 test system. Findings indicate that the DRL method rapidly acquires a robust control policy capable of securing sufficient SOL across various operating conditions. The approach is benchmarked against a rule-based lookup table method and a DRL variant restricted to discrete actions. Results demonstrate that the hybrid DRL controller consistently outperforms the alternatives across all evaluated test cases, highlighting the advantages of jointly optimizing discrete and continuous variables for greater flexibility and operational efficiency.

The Smart Grid (SG) seeks to overcome the limitations of conventional power systems by integrating renewable energy sources. It leverages wireless sensor networks (WSNs) to improve monitoring of power generation equipment. However, the open and expansive deployment of WSNs in SG introduces new vulnerabilities and potential attacks. This paper introduces a novel security framework for WSNs in SG that utilizes public key infrastructure (PKI). The core concept involves distributing the duties of the certification authority (CA) across multiple sensor nodes to guarantee service availability and scalability. A new lightweight clustering method is developed to select these nodes, accounting for their trust values and remaining energy. This framework delivers essential security features, including authentication and data confidentiality, while countering threats such as Sybil attacks and eavesdropping. Comprehensive simulations with OMNET++ and the Castalia framework demonstrate the clustering algorithm’s effectiveness: nearly all sensors join clusters, and the number of cluster heads remains consistent even when malicious nodes are present, highlighting the framework’s resilience.