European Journal of Emerging Cybersecurity and Information Protection
type: channel
description: ISSN: Applied Publication Frequency: Halfyearly Open Access | International Journal | Double Blinded Peer Reviewed Journal
publisher: The Parthenon Frontiers
language: en
publication name: European Journal of Emerging Cybersecurity and Information Protection
copyright: All articles published by The Parthenon Frontiers and its associated journals are distributed under the terms of the Creative Commons Attribution (CC BY 4.0) International License unless otherwise stated. Authors retain full copyright of their published work. By submitting their manuscript, authors agree to grant The Parthenon Frontiers a non-exclusive license to publish, archive, and distribute the article worldwide. Authors are free to: Share their article on personal websites, institutional repositories, or social media platforms. Reuse their content in future works, presentations, or educational materials, provided proper citation of the original publication.
items: Toward Unified Interpretability and Robustness in Machine Learning–Based Anomaly Detection Across Industrial, Network, Financial, and Cyber-Physical Domains
items: Regulatory Convergence of Blockchain, Artificial Intelligence, and Legal Infrastructure: Institutional Clarity, Technological Governance, and the Future of Digital Trust
items: Integrative Data Processing and Optimized Machine Learning Architectures for Advanced Electricity and Retail Demand Forecasting in High-Dimensional Environments
items: Integrative Artificial Intelligence Architectures for Supply Chain Optimization and Market Intelligence: A Deep Synthesis of Forecasting, Sentiment Analytics, and Business Concept Innovation
items: Integrating Business Intelligence Frameworks for Enhanced Organizational Decision-Making: Theoretical and Empirical Synthesis
items: Integrated Intelligent Maintenance Architectures for Hydro-Generator Excitation Systems: A Knowledge-Driven and Machine-Assisted Diagnostic Paradigm
items: Information Cartography and Secure Data Intelligence: Integrating GIS Metaphors, Business Analytics, and Cyber-Governance for Complex Digital Ecosystems
items: 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
items: NAVIGATING THE DIGITAL MINEFIELD: A SYSTEMATIC REVIEW OF COLLATERAL DAMAGE IN CYBER WARFARE AND ITS MITIGATION
items: IMPROVING USER COMPREHENSION AND CONTROL OF LOCAL DIFFERENTIAL PRIVACY THROUGH VISUAL INTERFACES
items: AUGMENTING WAZUH SIEM WITH MACHINE LEARNING FOR ADVANCED CYBER THREAT ANALYTICS
items: ASSESSING END-USER RESILIENCE TO PHISHING: A STUDY ON EDUCATIONAL INTERVENTIONS AND SIMULATED ATTACKS IN A CROATIAN UNIVERSITY
items: AN ANALYTICAL SURVEY OF IMMERSIVE TECHNOLOGIES FOR ENHANCING CYBER SITUATIONAL AWARENESS
items: A TRUST-BASED INCENTIVE FRAMEWORK FOR FEDERATED LEARNING IN EDGE ENVIRONMENTS WITH DIVERSE PARTICIPANTS
items: A STATIC ANALYSIS FRAMEWORK UTILIZING LARGE LANGUAGE MODELS FOR IDENTIFYING MALICIOUS OFFICE OPEN XML FILES

Toward Unified Interpretability and Robustness in Machine Learning–Based Anomaly Detection Across Industrial, Network, Financial, and Cyber-Physical Domains
type: item
description: 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 epistemol…
subject: Articles
  topic: section
subject: Anomaly detection
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Industrial analytics
  topic: keywords
subject: Network security
  topic: keywords
creator: Johannes K. Albrecht
creator: Lukas M. Reinhardt
rights: Copyright (c) 2026 Johannes K. Albrecht, Lukas M. Reinhardt (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-08
publication date: 2026-01-08
starting page: 1
ending page: 5

Regulatory Convergence of Blockchain, Artificial Intelligence, and Legal Infrastructure: Institutional Clarity, Technological Governance, and the Future of Digital Trust
type: item
description: 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 …
subject: Articles
  topic: section
subject: Blockchain governance
  topic: keywords
subject: Artificial intelligence in law
  topic: keywords
subject: Crypto regulation
  topic: keywords
subject: Digital trust
  topic: keywords
creator: Dr. Alexei Moreau
creator: Dr. Elena Varga
rights: Copyright (c) 2025 Dr. Alexei Moreau, Dr. Elena Varga (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-13
publication date: 2025-11-13
starting page: 17
ending page: 21

Integrative Data Processing and Optimized Machine Learning Architectures for Advanced Electricity and Retail Demand Forecasting in High-Dimensional Environments
type: item
description: 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-analytica…
subject: Articles
  topic: section
subject: Electricity demand forecasting
  topic: keywords
subject: hybrid machine learning
  topic: keywords
subject: big data analytics
  topic: keywords
subject: deep learning architectures
  topic: keywords
creator: Dr. Mateo Alvarez
creator: Dr. Sofia Leclerc
creator: Dr. Henrik Sørensen
creator: Dr. Valeria Conti
rights: Copyright (c) 2025 Dr. Mateo Alvarez, Dr. Sofia Leclerc, Dr. Henrik Sørensen, Dr. Valeria Conti (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-21
publication date: 2025-09-21
starting page: 9
ending page: 16

Integrative Artificial Intelligence Architectures for Supply Chain Optimization and Market Intelligence: A Deep Synthesis of Forecasting, Sentiment Analytics, and Business Concept Innovation
type: item
description: 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…
subject: Articles
  topic: section
subject: Artificial intelligence integration
  topic: keywords
subject: supply chain optimization
  topic: keywords
subject: demand forecasting
  topic: keywords
subject: sentiment analysis
  topic: keywords
creator: Dr. Rafael Domínguez
rights: Copyright (c) 2025 Dr. Rafael Domínguez (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-07-16
publication date: 2025-07-16
starting page: 1
ending page: 8

Integrating Business Intelligence Frameworks for Enhanced Organizational Decision-Making: Theoretical and Empirical Synthesis
type: item
description: 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 partic…
subject: Articles
  topic: section
subject: Business intelligence
  topic: keywords
subject: decision-making quality
  topic: keywords
subject: analytics capability
  topic: keywords
subject: organizational performance
  topic: keywords
creator: Dr. Alejandro Martínez
creator: Dr. Sofia Lindstrom
creator: Dr. Marco De Santis
rights: Copyright (c) 2025 Dr. Alejandro Martínez, Dr. Sofia Lindstrom, Dr. Marco De Santis (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-05-25
publication date: 2025-05-25
starting page: 21
ending page: 25

Integrated Intelligent Maintenance Architectures for Hydro-Generator Excitation Systems: A Knowledge-Driven and Machine-Assisted Diagnostic Paradigm
type: item
description: 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…
subject: Articles
  topic: section
subject: Hydro-generator excitation systems
  topic: keywords
subject: intelligent maintenance
  topic: keywords
subject: expert systems
  topic: keywords
subject: knowledge engineering
  topic: keywords
creator: Dr. Aleksandar Petrovic
creator: Dr. Katarina Müller
rights: Copyright (c) 2025 Dr. Aleksandar Petrovic, Dr. Katarina Müller (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-04-11
publication date: 2025-04-11
starting page: 16
ending page: 20

Information Cartography and Secure Data Intelligence: Integrating GIS Metaphors, Business Analytics, and Cyber-Governance for Complex Digital Ecosystems
type: item
description: 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 place…
subject: Articles
  topic: section
subject: Information cartography
  topic: keywords
subject: data analytics
  topic: keywords
subject: business intelligence
  topic: keywords
subject: data governance
  topic: keywords
creator: Dr. Elias Moreau
rights: Copyright (c) 2025 Dr. Elias Moreau (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-03-23
publication date: 2025-03-23
starting page: 10
ending page: 15

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
type: item
description: 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 f…
subject: Articles
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: demand forecasting
  topic: keywords
subject: supply chain performance
  topic: keywords
subject: neural networks
  topic: keywords
creator: Dr. Mateo R. Alvarez
creator: Prof. Léa Moreau
rights: Copyright (c) 2025 Dr. Mateo R. Alvarez, Prof. Léa Moreau (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-01-12
publication date: 2025-01-12
starting page: 1
ending page: 9

NAVIGATING THE DIGITAL MINEFIELD: A SYSTEMATIC REVIEW OF COLLATERAL DAMAGE IN CYBER WARFARE AND ITS MITIGATION
type: item
description: 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.
subject: Articles
  topic: section
subject: Cybersecurity
  topic: keywords
subject: Cyber Warfare
  topic: keywords
subject: Offensive Cyber Operations
  topic: keywords
subject: Collateral Damage
  topic: keywords
creator: Dr. Samuel K. Mensah
creator: Prof. Zinaida Zuyeva
creator: Prof. Alla Kornilova
rights: Copyright (c) 2024 European Journal of Emerging Cybersecurity and Information Protection
date: 2024-12-30
publication date: 2024-12-30
starting page: 77
ending page: 94

IMPROVING USER COMPREHENSION AND CONTROL OF LOCAL DIFFERENTIAL PRIVACY THROUGH VISUAL INTERFACES
type: item
description: 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.
subject: Articles
  topic: section
subject: Local Differential Privacy (LDP)
  topic: keywords
subject: User Experience (UX)
  topic: keywords
subject: Visual Controls
  topic: keywords
subject: Internet of Things (IoT)
  topic: keywords
creator: Dr. Yasmine El-Gamal
creator: Prof. Rina Deshmukh
rights: Copyright (c) 2024 European Journal of Emerging Cybersecurity and Information Protection
date: 2024-12-29
publication date: 2024-12-29
starting page: 69
ending page: 76

AUGMENTING WAZUH SIEM WITH MACHINE LEARNING FOR ADVANCED CYBER THREAT ANALYTICS
type: item
description: 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 cy…
subject: Articles
  topic: section
subject: Wazuh
  topic: keywords
subject: SIEM
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Threat Detection
  topic: keywords
creator: Dr. Ali R. Al-Harthy
creator: Dr. Hassan Ben Youssef
creator: Dr. Noura Al-Mutairi
rights: Copyright (c) 2024 European Journal of Emerging Cybersecurity and Information Protection
date: 2024-12-29
publication date: 2024-12-29
starting page: 57
ending page: 67

ASSESSING END-USER RESILIENCE TO PHISHING: A STUDY ON EDUCATIONAL INTERVENTIONS AND SIMULATED ATTACKS IN A CROATIAN UNIVERSITY
type: item
description: 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.
subject: Articles
  topic: section
subject: Phishing
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: End-User Awareness
  topic: keywords
subject: Simulated Attacks
  topic: keywords
creator: Dr. Marko D. Horvat
creator: Dr. Tomislav P. Radić
rights: Copyright (c) 2024 European Journal of Emerging Cybersecurity and Information Protection
date: 2024-12-25
publication date: 2024-12-25
starting page: 44
ending page: 56

AN ANALYTICAL SURVEY OF IMMERSIVE TECHNOLOGIES FOR ENHANCING CYBER SITUATIONAL AWARENESS
type: item
description: 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.
subject: Articles
  topic: section
subject: Cybersecurity
  topic: keywords
subject: Cyber Situational Awareness (Cyber SA)
  topic: keywords
subject: Immersive Analytics
  topic: keywords
subject: Virtual Reality (VR)
  topic: keywords
creator: Dr. Nadia M. Suleiman
creator: Dr. Henrik L. Olsen
rights: Copyright (c) 2024 European Journal of Emerging Cybersecurity and Information Protection
date: 2024-12-22
publication date: 2024-12-22
starting page: 35
ending page: 43

A TRUST-BASED INCENTIVE FRAMEWORK FOR FEDERATED LEARNING IN EDGE ENVIRONMENTS WITH DIVERSE PARTICIPANTS
type: item
description: 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.
subject: Articles
  topic: section
subject: Federated Learning
  topic: keywords
subject: Edge Computing
  topic: keywords
subject: Incentive Mechanism
  topic: keywords
subject: Trust Management
  topic: keywords
creator: Dr. Tobias Muller
creator: Chan Chiang
creator: Yuan Hsiao
rights: Copyright (c) 2024 European Journal of Emerging Cybersecurity and Information Protection
date: 2024-12-17
publication date: 2024-12-17
starting page: 14
ending page: 34

A STATIC ANALYSIS FRAMEWORK UTILIZING LARGE LANGUAGE MODELS FOR IDENTIFYING MALICIOUS OFFICE OPEN XML FILES
type: item
description: 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.
subject: Articles
  topic: section
subject: Pulmonary blastoma
  topic: keywords
subject: Biphasic tumor
  topic: keywords
subject: Lung neoplasm
  topic: keywords
subject: Case report
  topic: keywords
creator: Dr. Carlos M. Ruiz
creator: Prof. Anna Petrova
rights: Copyright (c) 2024 European Journal of Emerging Cybersecurity and Information Protection
date: 2024-12-11
publication date: 2024-12-11
starting page: 1
ending page: 13