European Journal of Emerging Cybersecurity and Information Protection
ISSN: Applied Publication Frequency: Halfyearly Open Access | International Journal | Double Blinded Peer Reviewed Journal
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Toward Unified Interpretability and Robustness in Machine Learning–Based Anomaly Detection Across Industrial, Network, Financial, and Cyber-Physical Domains
Anomaly detection has emerged as one of the most intellectually complex and practically consequential subfields of machine learning, driven by the accelerating digitization of industrial processes, networked infrastructures, financial systems, and cyber-physical environments. Across these domains, anomalies often represent rare, evolving, and context-dependent deviations whose significance is not merely statistical but operational, economic, and ethical. This research article develops a comprehensive, theory-driven, and empirically grounded synthesis of machine learning–based anomaly detection by integrating insights from industrial process monitoring, network intrusion detection, financial fraud analysis, healthcare Internet of Things security, and large-scale data systems. Drawing on a broad and deliberately heterogeneous body of literature, the article advances a unified interpretive framework that explains why anomaly detection remains resistant to universal solutions despite decades of algorithmic innovation. Particular emphasis is placed on comparative methodological perspectives, including classical statistical approaches, shallow machine learning methods, kernel-based novelty detection, clustering paradigms, and deep learning architectures. The analysis is anchored by contemporary empirical findings in industrial screw driving data, which illustrate how algorithmic performance is inseparable from domain semantics, feature engineering choices, and evaluation protocols (West and Deuse, 2024). Rather than summarizing prior work, the article expands each conceptual strand through historical development, theoretical debate, and critical comparison, exposing persistent tensions between accuracy, interpretability, adaptability, and computational feasibility. The methodology section articulates a text-based comparative research design that synthesizes cross-domain findings without relying on mathematical formalism or visual artifacts, thereby foregrounding epistemological assumptions and methodological limitations. Results are presented as interpretive patterns grounded in literature-based evidence, highlighting recurring phenomena such as sensitivity to hyperparameter tuning, dataset bias, and the contextual ambiguity of ground truth labels. The discussion extends these findings into a broader theoretical discourse on the future of anomaly detection research, arguing that progress depends less on novel architectures than on integrative evaluation philosophies and domain-aware learning paradigms. The article concludes by outlining a research agenda that prioritizes interpretability, cross-domain generalization, and ethical accountability as central criteria for next-generation anomaly detection systems.
Copyright (c) 2026 Johannes K. Albrecht, Lukas M. Reinhardt (Author) https://creativecommons.org/licenses/by/4.0
Regulatory Convergence of Blockchain, Artificial Intelligence, and Legal Infrastructure: Institutional Clarity, Technological Governance, and the Future of Digital Trust
The accelerating convergence of blockchain technology, artificial intelligence, and digitally mediated legal infrastructures has introduced profound challenges and opportunities for contemporary governance, institutional design, and regulatory theory. While blockchain systems promise decentralization, immutability, and trust minimization, their rapid diffusion across financial markets, healthcare, supply chains, and legal services has exposed significant regulatory ambiguities and systemic risks. Artificial intelligence, simultaneously transforming professional judgment, legal reasoning, and data-intensive decision-making, further complicates the institutional landscape by introducing algorithmic opacity, automation bias, and new forms of socio-technical power. This article develops an extensive, theoretically grounded, and empirically informed analysis of how emerging regulatory initiatives—particularly legislative efforts aimed at clarifying the legal status of blockchain tokens and crypto-exchange infrastructures—reshape the governance of digital trust in complex socio-technical systems. Drawing strictly upon the provided body of interdisciplinary literature, this research situates contemporary blockchain regulation within a longer historical trajectory of legal adaptation to disruptive technologies. It integrates scholarship on distributed ledger architectures, smart contracts, artificial intelligence in law, big data governance, and trust theory to argue that regulatory clarity is not merely a compliance mechanism but a constitutive force that stabilizes technological meaning, allocates responsibility, and enables institutional legitimacy. Particular attention is given to legislative efforts reported in 2020 that sought to clarify the legal classification of blockchain tokens and the operational boundaries of crypto exchanges, positioning these initiatives as pivotal inflection points in the maturation of blockchain governance frameworks. Methodologically, the study adopts a qualitative doctrinal–conceptual approach, combining interpretive legal analysis with comparative technological governance theory. Rather than empirical experimentation, the research synthesizes regulatory texts, scholarly debates, and sectoral use cases to generate analytically rich findings about the evolving relationship between law, code, and institutional authority. The results demonstrate that regulatory clarity mitigates fragmentation across jurisdictions, reduces uncertainty in innovation ecosystems, and enables the responsible integration of AI-driven systems within blockchain-enabled infrastructures. The discussion extends these findings by engaging deeply with competing scholarly perspectives on decentralization, trust-free systems, algorithmic governance, and legal automation. It critically evaluates claims that blockchain can eliminate the need for legal institutions, demonstrating instead that law remains central in structuring accountability, resolving disputes, and governing socio-technical complexity. The article concludes by articulating a future research agenda focused on hybrid regulatory models, adaptive governance mechanisms, and the ethical co-design of legal and technological systems capable of sustaining digital trust at scale.
Copyright (c) 2025 Dr. Alexei Moreau, Dr. Elena Varga (Author) https://creativecommons.org/licenses/by/4.0
Integrative Data Processing and Optimized Machine Learning Architectures for Advanced Electricity and Retail Demand Forecasting in High-Dimensional Environments
Demand forecasting has long stood at the epistemic core of operations management, energy systems planning, and data-driven economic coordination, yet its theoretical and methodological evolution has become increasingly complex with the emergence of big data, deep learning, and hybrid optimization architectures. Across electricity markets and retail ecosystems, forecasting errors no longer merely represent statistical inefficiencies but propagate systemic distortions across pricing, inventory, energy security, and algorithmic coordination mechanisms. This article develops a theoretically grounded and methodologically integrative research framework for demand forecasting that synthesizes optimized support vector machine learning, deep neural architectures, and data preprocessing pipelines across high-dimensional, nonlinear, and volatile demand environments. Drawing on a cross-sectoral corpus of literature in energy forecasting, retail analytics, machine learning, and algorithmic coordination, the study elaborates a composite modeling philosophy grounded in signal decomposition, feature purification, and optimization-based learning. A central theoretical anchor is the composite electricity demand forecasting framework proposed by Jiang, Li, Liu, and Gao, which demonstrated that data preprocessing combined with optimized support vector machines yields structurally superior demand representations under nonlinear and noisy conditions (Jiang et al., 2020). This work extends that insight by positioning such composite modeling strategies within a broader epistemological framework that incorporates attention mechanisms, probabilistic deep learning, promotional information encoding, and algorithmic feedback effects. Rather than treating forecasting as a single-model exercise, this study conceptualizes it as a layered epistemic process in which raw data are iteratively transformed into structured predictive knowledge. Methodologically, the research adopts a conceptual-analytical synthesis approach that integrates the theoretical foundations of time series analysis, statistical learning theory, deep recurrent architectures, and operational decision systems. This approach allows for the interpretation of empirical findings reported across the literature as components of a unified forecasting ontology. The results section articulates how composite learning systems systematically outperform monolithic models by reducing noise sensitivity, capturing inter-temporal dependencies, and enabling adaptive learning in non-stationary environments, as evidenced across electricity demand, retail sales, wind power, and fashion forecasting studies. The discussion advances a critical theory of algorithmic forecasting, arguing that improved demand prediction is not a neutral technological advancement but a structural force that reshapes coordination, competition, and market behavior. It draws on scholarship concerning algorithmic collusion, big data analytics, and promotional forecasting to demonstrate how predictive systems now function as both analytical instruments and market actors. The article concludes by proposing a future research agenda that situates demand forecasting within a broader socio-technical system of automated decision-making, energy transition, and digital commerce.
Copyright (c) 2025 Dr. Mateo Alvarez, Dr. Sofia Leclerc, Dr. Henrik Sørensen, Dr. Valeria Conti (Author) https://creativecommons.org/licenses/by/4.0
Integrative Artificial Intelligence Architectures for Supply Chain Optimization and Market Intelligence: A Deep Synthesis of Forecasting, Sentiment Analytics, and Business Concept Innovation
The contemporary global economy is increasingly shaped by complex, data-intensive, and interdependent supply chain and marketing ecosystems, in which organizations are required to process massive volumes of heterogeneous information while responding to highly volatile consumer behavior and competitive pressures. The integration of artificial intelligence and machine learning has emerged as one of the most consequential transformations in this environment, offering new epistemic, predictive, and strategic capabilities across operational and market-facing functions. This study develops a comprehensive and theoretically grounded synthesis of how advanced computational models, including deep learning architectures, sentiment analysis frameworks, and hybrid forecasting systems, can be integrated into supply chain management and marketing strategy to generate sustained competitive advantage. Building on the foundational argument that AI-enabled systems create superior decision quality and strategic agility in organizational networks (Muthaluri, 2024), this article situates algorithmic intelligence not merely as a technological tool but as an institutional and cognitive extension of the firm. The paper draws on a wide spectrum of interdisciplinary scholarship, ranging from natural language processing and recurrent neural networks to business model innovation, open innovation, and co-creation theory. Time series forecasting models such as ARIMA, LSTM, and hybrid neural networks are examined in relation to their ability to capture non-linear demand dynamics, a capability that has become essential in multichannel and digitally mediated retail environments (Siami-Namini et al., 2018; Zhang, 2003; Abbasimehr et al., 2020). In parallel, sentiment analysis methodologies grounded in both lexicon-based and deep learning approaches are analyzed as mechanisms for translating unstructured textual data from social media and digital platforms into actionable market intelligence (Pang et al., 2002; Socher et al., 2013; Zhang et al., 2011). These analytical capabilities are further embedded within broader frameworks of entrepreneurial opportunity recognition, business concept development, and innovation ecosystems, drawing on the extensive body of work on open innovation, crowdsourcing, and value co-creation (Palavesh, 2019; Palavesh, 2021; Palavesh, 2022). Methodologically, the article adopts a qualitative–theoretical synthesis approach, integrating findings from computational modeling, operations management, and strategic management to build a unified conceptual architecture. Rather than presenting numerical experimentation, the study develops a layered interpretive framework that explains how data flows, algorithmic learning, and organizational decision-making interact to produce superior forecasting accuracy, more responsive supply networks, and more precise marketing interventions. Particular attention is devoted to the epistemological implications of AI-driven systems, including their capacity to reshape managerial cognition, redistribute power within value chains, and redefine the boundaries of the firm. The results of this integrative analysis demonstrate that organizations that strategically combinei embed machine learning models into both upstream and downstream processes achieve not only improved demand prediction and inventory efficiency but also deeper consumer insight and more adaptive business models. These effects are amplified when AI systems are aligned with open innovation practices and customer co-creation, enabling firms to continuously refine offerings in response to real-time market signals (Muthaluri, 2024; Palavesh, 2021). The discussion situates these findings within broader debates about technological determinism, regulatory governance, and ethical responsibility, emphasizing that the competitive benefits of AI must be balanced against risks of opacity, bias, and systemic vulnerability (Bhaskar et al., 2020).
Copyright (c) 2025 Dr. Rafael Domínguez (Author) https://creativecommons.org/licenses/by/4.0
Integrating Business Intelligence Frameworks for Enhanced Organizational Decision-Making: Theoretical and Empirical Synthesis
Business intelligence has evolved from a set of fragmented reporting tools into a comprehensive organizational capability that fundamentally reshapes how decisions are conceived, evaluated, and implemented. Over the past three decades, organizations across industries have increasingly relied on business intelligence systems to cope with escalating data volumes, growing environmental uncertainty, and heightened competitive pressure. This research article provides an extensive, theory-driven and empirically grounded examination of business intelligence as an integrated framework for organizational decision-making. Drawing strictly on the provided body of literature, the article synthesizes classical and contemporary perspectives on business intelligence, analytics, data mining, and artificial intelligence to develop a holistic understanding of how business intelligence contributes to decision quality, strategic alignment, and organizational performance. The study is grounded in the conceptual foundations of business intelligence frameworks, with particular attention to early integrative models that positioned business intelligence as a bridge between data, information, and managerial action, such as the framework articulated by Kokin and Wang (2013). Building on this foundation, the article critically examines the evolution of business intelligence capabilities, the role of organizational culture and leadership, the integration of advanced analytics and artificial intelligence, and the behavioral and cognitive dimensions of decision-making. The methodological approach is qualitative and interpretive, relying on systematic theoretical analysis rather than statistical modeling, in line with the objective of developing deep conceptual clarity and scholarly integration. The findings suggest that business intelligence effectiveness is not solely a function of technological sophistication but is deeply embedded in organizational structures, managerial cognition, user participation, and analytical orientation. The discussion elaborates on competing scholarly viewpoints, unresolved debates, and contextual contingencies, including crisis situations such as the COVID-19 pandemic. The article concludes by identifying critical limitations in current research and proposing future research directions that emphasize ethical considerations, organizational learning, and the democratization of analytics. Overall, this research contributes a comprehensive, publication-ready synthesis that advances theoretical understanding and provides a robust foundation for future empirical inquiry into business intelligence and decision-making.
Copyright (c) 2025 Dr. Alejandro Martínez, Dr. Sofia Lindstrom, Dr. Marco De Santis (Author) https://creativecommons.org/licenses/by/4.0
Integrated Intelligent Maintenance Architectures for Hydro-Generator Excitation Systems: A Knowledge-Driven and Machine-Assisted Diagnostic Paradigm
The reliability and operational continuity of hydroelectric power plants depend critically on the performance of hydro-generator excitation systems, which regulate voltage stability, reactive power control, and transient response under dynamic grid conditions. Over the past several decades, the evolution of intelligent maintenance systems has transformed the way complex electromechanical infrastructures are monitored, diagnosed, and sustained. This research article develops an extensive theoretical and analytical investigation into intelligent, knowledge-based, and machine-assisted maintenance architectures for hydro-generator excitation systems, with particular emphasis on real-time embedded maintenance frameworks. Grounded strictly in established scholarly literature, the study synthesizes developments in expert systems, knowledge engineering, machine learning, and real-time diagnostics within power generation environments. A central contribution of this work is the conceptual integration of embedded real-time maintenance principles demonstrated in early excitation system research with later advances in expert systems and machine intelligence applied to industrial diagnostics. The article critically examines how traditional rule-based expert systems, domain ontologies, and knowledge representation methods have been progressively augmented by data-driven learning techniques, while retaining interpretability and operational trustworthiness required in safety-critical energy infrastructures. Drawing on seminal works in artificial intelligence, knowledge engineering, and power plant diagnostics, the study articulates a comprehensive methodological framework for designing intelligent maintenance systems that operate continuously, adapt to evolving system states, and support human decision-makers without displacing them. The discussion highlights the enduring relevance of early embedded diagnostic architectures for excitation systems, particularly those emphasizing real-time signal acquisition, fault symptom mapping, and expert inference mechanisms, and situates them within contemporary debates on human–machine governance and intelligent automation. By offering a deeply elaborated, non-summarized, and theoretically expansive analysis, this article addresses a significant literature gap: the absence of an integrative, conceptually unified account of intelligent maintenance systems for hydro-generator excitation subsystems that bridges classical expert system design with modern machine-assisted reasoning. The findings underscore that sustainable innovation in power plant maintenance does not arise from algorithmic novelty alone, but from coherent system architectures that harmonize embedded sensing, domain knowledge, and adaptive intelligence within robust socio-technical frameworks.
Copyright (c) 2025 Dr. Aleksandar Petrovic, Dr. Katarina Müller (Author) https://creativecommons.org/licenses/by/4.0
Information Cartography and Secure Data Intelligence: Integrating GIS Metaphors, Business Analytics, and Cyber-Governance for Complex Digital Ecosystems
The accelerating complexity of contemporary digital ecosystems has profoundly altered how organizations collect, interpret, govern, and secure data. As data volumes expand and analytical paradigms diversify across business intelligence, healthcare, Internet of Things environments, and regulated clinical systems, the challenge has shifted from mere data processing to meaningful sense-making, trustworthy governance, and secure dissemination. This research article develops a comprehensive theoretical and analytical investigation into the convergence of information cartography, data analytics, and cybersecurity governance as a unified framework for understanding and managing non-spatial and quasi-spatial data landscapes. Drawing conceptually from the foundational notion of information cartography, which applies cartographic and geographic metaphors to non-spatial data visualization and cognition, this study situates such metaphors within modern business intelligence systems, clinical data infrastructures, and IoT-driven edge and fog computing environments. The work further examines how secure coding practices, data integrity regulations, and privacy-preserving mechanisms interact with visualization and analytics to shape decision-making reliability and institutional accountability. Grounded strictly in the provided scholarly and regulatory references, the article employs an interpretive, literature-driven methodology to synthesize perspectives from geographic information systems theory, business analytics, clinical data governance, and cybersecurity engineering. Rather than proposing a technical artifact or empirical experiment, the study articulates a deep theoretical integration that explains why spatial metaphors remain cognitively powerful in non-spatial domains, how modern analytics operationalize these metaphors for strategic intelligence, and why security and governance frameworks are indispensable to sustaining trust in such systems. Particular emphasis is placed on regulated domains such as healthcare and clinical trials, where data integrity, auditability, and compliance intersect with advanced analytics and visualization practices. The findings demonstrate that information cartography functions not merely as a visualization technique but as a cognitive and organizational infrastructure that aligns analytical reasoning, governance mechanisms, and security controls. By interpreting results through the lenses of fog and edge computing, secure software engineering, and international regulatory standards, the article advances a holistic conceptual model for secure data intelligence. The discussion elaborates theoretical implications, addresses scholarly debates, and outlines future research trajectories focused on ethically grounded, resilient, and intelligible data ecosystems.
Copyright (c) 2025 Dr. Elias Moreau (Author) https://creativecommons.org/licenses/by/4.0
Artificial Intelligence–Driven Demand Forecasting And Supply Chain Performance: A Deep Integrative Analysis Of Neural, Hybrid, And Context-Aware Models In Contemporary Retail And Industrial Networks
The accelerating integration of artificial intelligence into supply chain management has transformed the epistemic foundations of forecasting, coordination, and performance measurement across industrial and retail ecosystems. Traditional statistical and econometric forecasting approaches, long dominant in operations research and supply chain planning, are increasingly unable to cope with the volatility, nonlinearity, and contextual interdependence that characterize contemporary demand environments. Against this backdrop, artificial intelligence, particularly in the form of artificial neural networks, deep learning architectures, and hybrid computational models, has emerged as a central driver of predictive accuracy and operational agility. This article develops a comprehensive, theoretically grounded and empirically informed synthesis of how artificial intelligence–based demand forecasting systems reshape supply chain performance across procurement, production, inventory management, and distribution. Anchored in the performance-oriented framework articulated by Mohsen (2023), the study integrates a wide body of neural forecasting, hybrid time-series modeling, and retail analytics literature to demonstrate how predictive intelligence operates not merely as a technological upgrade but as a structural reconfiguration of supply chain governance, responsiveness, and resilience. The analysis situates artificial intelligence forecasting within the historical evolution of supply chain theory, tracing its movement from deterministic planning models to stochastic and finally to adaptive learning systems. Through a qualitative meta-analytical methodology grounded in cross-domain literature, the study examines how multilayer perceptrons, long short-term memory networks, multimodal architectures, and hybrid ARIMA–ANN systems enhance the informational symmetry between demand signals and operational responses. By synthesizing evidence from retail, energy, textile, e-grocery, and fast-moving consumer goods sectors, the article shows that artificial intelligence not only reduces forecast error but also changes how organizations perceive risk, manage uncertainty, and allocate resources. The results indicate that artificial intelligence forecasting generates performance improvements through three interlinked mechanisms: first, by capturing nonlinear demand patterns that elude traditional models; second, by integrating heterogeneous data sources such as promotions, weather, color preferences, and temporal dynamics; and third, by enabling real-time adaptive learning within planning systems. These mechanisms translate into tangible performance outcomes including lower inventory volatility, reduced stockouts, improved service levels, and higher financial efficiency. However, the study also identifies structural and epistemological limitations, including data dependency, model opacity, and organizational misalignment, which moderate the realized benefits of artificial intelligence adoption. By offering a theoretically expansive and critically balanced account of artificial intelligence in supply chain forecasting, this article advances scholarly understanding of how predictive technologies mediate the relationship between uncertainty and performance. It concludes that artificial intelligence should be conceptualized not merely as a forecasting tool but as a socio-technical infrastructure that redefines how modern supply chains sense, interpret, and respond to their environments, thereby shaping competitive advantage and systemic resilience in the digital economy.
Copyright (c) 2025 Dr. Mateo R. Alvarez, Prof. Léa Moreau (Author) https://creativecommons.org/licenses/by/4.0
NAVIGATING THE DIGITAL MINEFIELD: A SYSTEMATIC REVIEW OF COLLATERAL DAMAGE IN CYBER WARFARE AND ITS MITIGATION
Offensive cyber operations (OCOs) have rapidly evolved into a critical component of modern statecraft and conflict, offering diverse capabilities ranging from intelligence gathering to the disruption and destruction of adversary systems. However, the inherent characteristics of cyberspace—its pervasive interconnectedness, intricate complexity, and often ambiguous nature—introduce profound challenges, particularly concerning collateral damage. This comprehensive systematic literature review delves into the conceptualization, ramifications, and strategies for mitigating unintended harm arising from OCOs. By synthesizing insights from academic scholarship, military doctrine, and policy discourse, this review categorizes the multifaceted forms of collateral damage, scrutinizes the legal and ethical frameworks designed to govern such effects, and identifies prevailing approaches and future trajectories for minimizing inadvertent harm. The findings unequivocally highlight the urgent necessity for robust assessment methodologies, enhanced international collaboration, and the responsible development and deployment of cyber capabilities to safeguard non-combatant civilians and vital infrastructure from the far-reaching and often pervasive ripple effects of contemporary cyber warfare.
Copyright (c) 2024 European Journal of Emerging Cybersecurity and Information Protection
IMPROVING USER COMPREHENSION AND CONTROL OF LOCAL DIFFERENTIAL PRIVACY THROUGH VISUAL INTERFACES
The pervasive deployment of Internet of Things (IoT) devices, particularly in smart homes, has amplified concerns regarding user privacy. While Local Differential Privacy (LDP) offers a robust framework for preserving individual data privacy, its inherent mathematical complexity often renders it opaque to end-users, hindering effective privacy management. This article proposes and explores the design of intuitive visual controls aimed at enhancing user comprehension and control over LDP mechanisms. By translating abstract privacy parameters into tangible, interactive visual elements, we aim to bridge the gap between technical privacy guarantees and user expectations. This approach fosters a more user-centric privacy paradigm, empowering individuals to make informed decisions about their data sharing in connected environments.
AUGMENTING WAZUH SIEM WITH MACHINE LEARNING FOR ADVANCED CYBER THREAT ANALYTICS
The escalating sophistication of cyber threats necessitates a paradigm shift from traditional, signature-based security measures to more dynamic, intelligent defense mechanisms. This article explores the enhancement of Wazuh, a widely adopted open-source Security Information and Event Management (SIEM) solution, through the integration of machine learning techniques. The primary limitation of rule-based systems, such as high false-positive rates and an inability to detect novel threats, is a significant challenge for modern Security Operations Centers (SOCs). This study proposes and evaluates a hybrid framework that integrates both supervised (K-Nearest Neighbors, Random Forest, Naive Bayes, Logistic Regression, Support Vector Machine) and unsupervised (DBSCAN, K-Means, Isolation Forest) machine learning models into the Wazuh detection pipeline. By leveraging algorithms such as these, this work demonstrates the potential to significantly improve threat detection rates, reduce false positives, and automate complex security event analysis. This study details a comprehensive framework for data collection in a simulated enterprise environment, extensive preprocessing and feature engineering, the application of various machine learning models for threat identification, and a rigorous comparative analysis of their performance. The findings indicate that the Random Forest classifier achieves a superior accuracy of 97.2%, while the DBSCAN algorithm demonstrates 91.1% accuracy in anomaly detection, significantly enhancing the quality of alerts. Furthermore, the real-world viability is assessed through latency and scalability testing, confirming that the proposed system can operate effectively within the stringent time constraints of a real-time SOC. This fusion of machine learning with Wazuh's robust monitoring capabilities offers a formidable, cost-effective, and scalable solution for organizations, particularly Small and Medium-sized Enterprises (SMEs), to bolster their cybersecurity posture against an evolving threat landscape. The article further discusses the practical implications, limitations, and future research directions, emphasizing the synergy between automated systems and human expertise within a modern SOC.
ASSESSING END-USER RESILIENCE TO PHISHING: A STUDY ON EDUCATIONAL INTERVENTIONS AND SIMULATED ATTACKS IN A CROATIAN UNIVERSITY
Phishing remains a pervasive and evolving cybersecurity threat, consistently exploiting the human element as a primary vulnerability in organizational defenses. This comprehensive study investigates the efficacy of structured educational interventions combined with realistic simulated phishing attacks in bolstering end-user resilience against these threats within a prominent Croatian university. Employing a quasi-experimental design, the research involved a multi-phased approach comprising an initial baseline assessment, targeted educational modules, and subsequent simulated attacks. We meticulously analyzed behavioral responses, compromise rates, and their statistical associations with various demographic and contextual variables, including age, departmental affiliation, and professional qualifications. While individual interventions showed varying degrees of immediate impact, a critical finding emerged regarding the significant influence of temporal factors, particularly pre-holiday periods, on user susceptibility. These results underscore the inherent limitations of standalone awareness assessments and highlight the imperative for ongoing, highly contextualized, and integrated cybersecurity training methodologies. The findings offer practical guidance for academic institutions and other organizations seeking to develop more robust and adaptive phishing defense strategies that account for both human factors and environmental dynamics.
AN ANALYTICAL SURVEY OF IMMERSIVE TECHNOLOGIES FOR ENHANCING CYBER SITUATIONAL AWARENESS
The escalating complexity and frequency of cyber threats necessitate advanced tools for effective cyber defense. Traditional security systems, often relying on two-dimensional displays, struggle to represent the multi-faceted nature of modern cyber data, potentially overwhelming human analysts and causing data occlusion. This has led to a growing interest in immersive technologies—Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR)—as a means to enhance Cyber Situational Awareness (Cyber SA). This paper presents a systematic survey of the current landscape of immersive systems designed for cybersecurity. We follow a structured review methodology to identify, categorize, and analyze existing research, focusing on applications in threat visualization, security operations, and professional training. Our findings indicate that immersive analytics can significantly improve data comprehension, facilitate collaborative analysis, and provide engaging educational experiences. Based on our synthesis of the literature, we propose a novel reference framework that maps specific immersive visualization and interaction techniques to the core levels of situational awareness: perception, comprehension, and projection. This framework serves as a guide for the design and analysis of future Immersive Cyber Situational Awareness (ICSA) systems. Finally, we discuss the primary challenges, identify key research gaps, and propose future directions, including the integration of Artificial Intelligence (AI) and the need for large-scale empirical studies to unlock the full potential of immersive technologies in strengthening global cyber defenses.
A TRUST-BASED INCENTIVE FRAMEWORK FOR FEDERATED LEARNING IN EDGE ENVIRONMENTS WITH DIVERSE PARTICIPANTS
Federated Learning (FL) offers a privacy-preserving paradigm for collaborative model training, particularly well-suited for edge computing. However, the inherent heterogeneity of edge clients—encompassing data distribution, computational capabilities, and reliability—poses significant challenges to FL's effectiveness, including performance degradation and vulnerability to malicious participants. This article proposes a novel trust-based incentive mechanism designed to address these issues by dynamically evaluating client trustworthiness and adjusting incentives accordingly. Our framework integrates a multi-faceted trust score that considers contribution quality, reliability, and the detection of malicious behavior. By rewarding trustworthy contributions and penalizing unreliable actions, the proposed mechanism aims to enhance global model accuracy, improve robustness against attacks, and foster fairness among diverse participants. This approach encourages consistent, high-quality contributions while mitigating risks from untrustworthy clients, paving the way for more resilient and efficient federated learning deployments in real-world edge environments.
A STATIC ANALYSIS FRAMEWORK UTILIZING LARGE LANGUAGE MODELS FOR IDENTIFYING MALICIOUS OFFICE OPEN XML FILES
Office Open XML (OOXML) documents represent a primary vector for malware distribution, capitalizing on their ubiquitous presence in modern enterprise and personal computing. The inherent complexity of the OOXML format provides a fertile ground for concealing malicious payloads, which often evade traditional security measures. Conventional detection methods, which predominantly rely on signature-based scanning and predefined rules, are frequently outpaced by the rapid evolution of malware, particularly sophisticated threats like polymorphic code, zero-day exploits, and advanced social engineering tactics. This paper proposes a novel, in-depth static analysis framework that leverages the advanced contextual understanding and reasoning capabilities of Large Language Models (LLMs) to unmask malicious OOXML documents. Our methodology involves a systematic deconstruction of the OOXML package into a structured, human-readable JSON format. This comprehensive representation is then fed to an LLM, which, guided by a sophisticated, role-based prompt, performs a deep semantic analysis of the document’s constituent parts. The model scrutinizes everything from VBA macro code and XML relationship files to embedded objects and metadata for indicators of malicious intent. This approach transcends the limitations of simple pattern matching, enabling a holistic assessment of the document's structure and content. The framework demonstrates a high potential for accurately identifying malicious documents, including those that employ heavy obfuscation or novel attack vectors, thereby offering a significant and necessary advancement in the ongoing fight against document-based cyber threats.