Background: As social robots increasingly integrate into public spaces, comprehending their security implications becomes paramount. This study is conducted amidst the growing use of social robots in public spaces (SRPS), emphasising the necessity for tailored security standards for these unique robotic systems. Methods: In this systematic mapping study (SMS), we meticulously review and analyse existing literature from the Web of Science database, following guidelines by Petersen et al. We employ a structured approach to categorise and synthesise literature on SRPS security aspects, including physical safety, data privacy, cybersecurity, and legal/ethical considerations. Results: Our analysis reveals a significant gap in existing safety standards, originally designed for industrial robots, that need to be revised for SRPS. We propose a thematic framework consolidating essential security guidelines for SRPS, substantiated by evidence from a considerable percentage of the primary studies analysed. Conclusions: The study underscores the urgent need for comprehensive, bespoke security standards and frameworks for SRPS. These standards ensure that SRPS operate securely and ethically, respecting individual rights and public safety, while fostering seamless integration into diverse human-centric environments. This work is poised to enhance public trust and acceptance of these robots, offering significant value to developers, policymakers, and the general public.

This study presents a Systematic Literature Review (SLR) of methods and challenges related to distinguishing cybersecurity attacks and safety faults in Distributed Energy Resources (DER)-rich smart grids. The increasing integration of DERs has improved grid flexibility and sustainability, but has also introduced greater operational complexity and expanded the attack surface of smart grid infrastructures. In such environments, cyber-attacks and safety faults may produce similar anomalies, making accurate distinction a critical requirement for resilient and secure grid operation. This review examines the literature on anomaly detection and event classification in DER-rich smart grids towards distinguishing these anomalies and events, with a particular focus on approaches that could support the differentiation of malicious and non-malicious events. It analyzes the main categories of data used in the literature, including sensor data, smart meter data, network traffic, phasor measurements, and weather-related information. It also reviews machine learning and artificial intelligence techniques, including supervised, unsupervised, and deep learning approaches, and discusses their applicability, strengths, and limitations within different contexts. Furthermore, the review synthesizes evaluation practices, operational application domains, and key open challenges, including limited dataset realism, data quality issues, explainability, scalability, and model generalization. Existing testbeds are highlighted as essential for reproducing realistic grid conditions and validating approaches; however, in their current state, no testbed framework is directly applicable to distinguishing cybersecurity attacks from operational safety faults in an informed manner. Importantly, only 6 of the 25 included primary studies explicitly attempt to distinguish cyber-attacks from physical safety faults within a single evaluation setting. The review is therefore explicitly positioned as a combined systematic mapping and gap analysis: rather than demonstrating a mature solution space, it documents how sparsely the central distinction problem is addressed, characterizes the few existing attempts in depth, and derives a research agenda from this gap. Overall, the review identifies major research gaps and outlines directions for developing more safe and secure solutions for DER-rich smart grids.

With the global demand for cybersecurity professionals consistently exceeding supply, higher education institutions plays a critical role in addressing the skills gap. This requires aligning curricula not only with evolving workforce requirements but also with international standards that ensure consistency and quality. However, systematic approaches for identifying curricular strengths and gaps across universities remain underdeveloped. To address this gap, this paper introduces a methodological framework that integrates the Cyber Security Curricula 2017 guidelines with a curriculum maturity model to evaluate cybersecurity education across institutions. We demonstrate the framework through a comparative case study of three universities: Norwegian University of Science and Technology (NTNU), Østfold University College (HiØ), and The Virginia Polytechnic Institute and State University (Virginia Tech). Across three universities (NTNU, HiØ, Virginia Tech), the framework mapped 110 courses, with 44% Core Cybersecurity and 56% Cybersecurity-Related. NTNU excelled in Data Security, HiØ in Organizational and Human Security, and Virginia Tech in Societal Security. Common strengths included Cryptography, Risk Management, and Network Architectures, while persistent gaps appeared in Storage Security, Identity Management, Usable Security, and lifecycle aspects of System Security. These results highlight the value of a structured, evidence-based approach for identifying curricular strengths and weaknesses. This framework is designed to be replicable in other higher-education contexts seeking program improvement.