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Digital literacy is an essential competency that secondary school students must possess to face the rapid development of technology in the digital era. However, many students still lack adequate understanding of basic information technology concepts, digital security, and the responsible use of technology. This community service activity aimed to improve students' digital literacy through a basic information technology introduction program. The methods employed included preliminary observation, pre-test administration, educational sessions, hands-on practice, discussions, and evaluation through post-tests. The training materials covered computer hardware and software fundamentals, effective internet usage, digital security, and ethical behavior in digital environments. The results indicated a significant improvement in students’ knowledge and digital skills after participating in the program. Students became more capable of accessing, evaluating, and utilizing digital information critically while understanding the importance of cybersecurity and ethical technology use. Furthermore, the high level of participant engagement demonstrated that the program was relevant to students’ needs in today's digital society. Therefore, the basic information technology introduction program proved effective in enhancing digital literacy among secondary school students and can serve as a strategic initiative to prepare young generations to adapt to technological advancements and future digital challenges. Community Service, Digital Education, Digital Literacy, Information Technology, Secondary School

This study investigates the integration of digital twin technology and machine learning for predictive analysis in smart mechanical systems. The research emphasizes the role of intelligent computational frameworks in improving industrial monitoring, predictive maintenance, and operational efficiency within Industry 4.0 environments. A qualitative content analysis approach was employed by reviewing scientific literature, industrial reports, and previous studies related to digital twins, artificial intelligence, and predictive analytics. The findings indicate that digital twin architectures supported by machine learning algorithms can significantly enhance real-time monitoring, fault prediction accuracy, and maintenance optimization. The integration of IoT devices, cloud computing, and intelligent analytics also improves industrial sustainability, reduces operational downtime, and supports data-driven decision-making processes. Furthermore, the study identifies several technological challenges, including cybersecurity risks, data integration complexity, and computational limitations. Overall, the proposed intelligent digital twin framework provides a promising approach for future industrial innovation and sustainable smart mechanical system management

This study explores the role of artificial intelligence in strengthening cybersecurity threat detection frameworks for next-generation network environments. The rapid expansion of cloud computing, Internet of Things ecosystems, and distributed digital infrastructures has significantly increased cybersecurity risks and operational vulnerabilities. Traditional cybersecurity systems often struggle to detect sophisticated and evolving threats due to their dependence on static detection mechanisms. Using a qualitative research approach and content analysis method, this study examines recent developments in artificial intelligence, machine learning algorithms, and intelligent cybersecurity frameworks. The findings indicate that AI-driven cybersecurity systems improve real-time threat detection, anomaly identification, automated monitoring, and predictive security analysis. Machine learning technologies such as Random Forest, Support Vector Machine, and deep learning models demonstrate strong potential for enhancing intrusion detection accuracy and reducing false positive rates. The study also identifies critical challenges related to ethical governance, privacy protection, computational complexity, and adversarial attacks in AI-based cybersecurity systems

The rapid advancement of digital technology has significantly transformed various aspects of human life, including the education sector. In the Society 5.0 era, educational institutions are expected to integrate digital technologies into learning processes to develop adaptive, innovative, and globally competitive human resources. This study aims to analyze digital technology-based learning innovations in improving educational quality, identify their potential benefits, and examine the challenges and strategies for optimizing their implementation. This research employed a qualitative descriptive approach through a literature review by analyzing relevant academic sources, including scholarly journals, books, conference proceedings, and other scientific publications. The findings indicate that digital learning innovations are implemented through the utilization of Learning Management Systems (LMS), Artificial Intelligence (AI), Augmented Reality (AR), Virtual Reality (VR), interactive learning media, blended learning, and flipped classroom models. These technologies contribute to enhancing learning effectiveness, expanding educational accessibility, increasing student motivation and engagement, and fostering essential twenty-first-century competencies. Nevertheless, several challenges remain, including digital inequality, inadequate technological infrastructure, limited digital competencies among educators, and concerns related to digital ethics and cybersecurity. Therefore, effective implementation requires comprehensive strategies, such as strengthening teachers' digital competencies, improving educational infrastructure, developing adaptive curricula, integrating innovative learning media, and promoting digital literacy and ethical awareness. These efforts are expected to support the development of a more adaptive, inclusive, and high-quality education system that aligns with the demands of the Society 5.0 era.

The rapid advancement of Internet of Things (IoT) technologies has transformed conventional residential environments into intelligent smart housing systems capable of enhancing energy efficiency, security, comfort, healthcare services, and environmental sustainability. However, existing studies remain fragmented, focusing on specific technologies or applications rather than providing a comprehensive understanding of the smart housing ecosystem. Therefore, this study aims to systematically review the major technologies employed in IoT-based smart housing systems, examine their contributions to residential benefits, and identify the challenges and future opportunities associated with their implementation. This research adopted a Systematic Literature Review (SLR) approach using the Scopus database as the primary source of literature. The review process followed a structured screening and selection procedure, resulting in the inclusion of 26 studies that met the predefined eligibility criteria. The findings indicate that Artificial Intelligence, Blockchain, Edge Computing, Fog Computing, Cloud Computing, Digital Twin, Smart Grid technologies, renewable energy systems, and advanced sensor infrastructures constitute the primary technological foundations of modern smart housing systems. The reviewed studies indicate that these technologies contribute to energy optimization, cybersecurity enhancement, intelligent automation, healthcare monitoring, safety improvement, and sustainable resource management. However, challenges related to security, privacy, interoperability, scalability, computational complexity, and implementation costs remain important barriers to widespread adoption. In conclusion, IoT-based smart housing systems have evolved into integrated and intelligent residential ecosystems, and future development is expected to be driven by the convergence of Artificial Intelligence, distributed computing, Digital Twin technologies, and sustainable smart infrastructure solutions.  

Malware represents an evolving cybersecurity threat that demands more effective detection methods. Conventional signature-based detection systems have limitations in identifying new variants, driving the development of deep learning-based approaches. This research implements and evaluates four variants of the YOLOv11 algorithm (n, s, m, l) for malware classification based on visual image representation. The dataset consists of 22,056 malware and benign images, divided into 70% training, 15% validation, and 15% testing across 8 classes (adware, backdoor, benign, downloader, spyware, trojan, virus, worm). Each model was trained for 100 epochs with batch size 32 using Google Colab with GPU support. Results demonstrate that all variants achieve high accuracy (97.8%-98.1%) with YOLOv11m as the best performer (98.1%). YOLOv11n offers optimal balance between accuracy (97.9%) and efficiency (1.5M parameters, 0.3 ms/img inference) ideal for real-time applications. This research surpasses previous methods such as K-NN (97.18%) and hybrid CNN (96.55%) with superior inference speed (0.3-0.9 ms/img vs tens to hundreds of ms/img), proving the effectiveness of YOLOv11 for fast, accurate, and scalable malware detection.

Phishing is one of the most common and dangerous cyber attacks as it can trick users into providing sensitive information such as usernames, passwords, and personal data. This study aims to simulate Phishing techniques using the Zphisher tool as an educational medium to improve cybersecurity awareness. The method used is a quasi-experimental approach involving 30 participants exposed to Phishing scenarios in a controlled environment using Kali Linux. The results show that 83% of participants clicked the Phishing link and 60% entered their credentials, while 40% demonstrated awareness by avoiding further interaction. These findings indicate that cybersecurity awareness is still relatively low, but Phishing simulation is effective as an experiential learning tool.

This study aims to analyze the reconceptualization of national security in the digital era through the case of the 2021 Facebook data breach involving 533 million users across 106 countries. As digital technologies become increasingly integrated into governance, economic activities, and social interactions, cyber threats have emerged as a significant challenge to contemporary security frameworks. This research employs a qualitative descriptive approach using library research methods, drawing upon academic literature, official reports, and relevant policy documents. The findings indicate that national security has evolved from a traditional military-centered concept toward a broader and multidimensional framework that includes cyber threats as a form of non-traditional security challenge. The Facebook data breach demonstrates how cyber threats transcend geographical boundaries, involve complex attribution problems, and generate multidimensional impacts on individuals, societies, economies, and states. Through the lens of Securitization Theory developed by Buzan, Wæver, and de Wilde (1998), the incident illustrates how data security has become securitized as a matter of national and international concern. Furthermore, the Human Security framework proposed by UNDP (1994) highlights the vulnerability of individuals whose personal information becomes exposed in the digital environment. This study concludes that cybersecurity should be recognized as a fundamental pillar of national security in the digital age, requiring comprehensive strategies that integrate technological resilience, data protection regulations, human resource development, and international cooperation.

Recent Internet of Things (IoT) intrusion detection studies have reported near-perfect benchmark performance for Distributed Denial of Service (DDoS) detection, yet limited attention has been given to understanding how different traffic representations contribute to the detection process under highly imbalanced traffic conditions. This study presents an ablation-driven analysis to investigate the contribution of statistical and temporal representations for large-scale IoT DDoS detection using the CICIoT2023 dataset. Three experimental scenarios are evaluated, including statistical representation, temporal sequence representation, and hybrid statistical–temporal representation. Temporal representations are learned using a one-dimensional Convolutional Neural Network (1D-CNN) with lag-based traffic sequences, while ensemble tree-based classifiers are employed for final classification and representation analysis. In addition, multiple ablation configurations are designed to evaluate the impact of temporal dependency modeling and feature engineering strategies on detection performance. Experimental results show that statistical traffic representations remain highly effective for DDoS detection on CICIoT2023, achieving 99.36% accuracy and 99.31% weighted F1-score in the statistical representation scenario. Feature importance analysis further indicates that engineered statistical features contribute substantially more to the classification process than CNN-based temporal representations. Although temporal modeling captures sequential traffic behavior, its contribution is relatively limited and mainly acts as a complementary representation. Furthermore, the hybrid configuration produces only marginal improvements over the statistical representation alone. These findings highlight the importance of representation-level analysis for understanding the actual contribution of statistical and temporal modeling in modern IoT intrusion detection systems beyond relying solely on benchmark accuracy.

 The rapid adoption of smartphones among Indonesian digital natives has increased reliance on biometric authentication systems. However, empirical evidence regarding the relationship between user satisfaction and security risk awareness remains limited, particularly in developing-country contexts. This study investigates the behavioral dynamics of biometric security perception among 266 respondents, consisting of 221 high school students and 45 university students in Indonesia. A Python-based computational pipeline incorporating Akaike Information Criterion (AIC) validation and 1,000-iteration stochastic bootstrapping was employed to evaluate nonlinear behavioral patterns using Polynomial Regression and Ordinary Least Squares (OLS) multivariate analysis. The results confirm the existence of a nonlinear Security Paradox. While the overall population demonstrates a positive quadratic trajectory, the university student group exhibits a concave-down parabolic relationship (a=−0.0460), indicating a decline in perceived utility beyond a specific security threshold. The identified behavioral breaking point occurs at X≈5.45 (95% CI: 2.99–20.77), suggesting that excessive security hardening may reduce perceived usability and increase cognitive friction. Furthermore, the ablation analysis reveals that security risk awareness (p<0.001) is the strongest predictor of user satisfaction, exceeding the influence of daily usage intensity. Segment-level analysis further demonstrates behavioral divergence between respondent groups. High school students exhibit relatively uniform satisfaction toward biometric systems, whereas university students display greater variability and more critical perceptions regarding authentication friction. These findings indicate that highly rigid security configurations may become less effective for users with higher digital literacy and risk awareness. This study contributes a computationally validated behavioral framework for understanding security–utility trade-offs and provides a conceptual foundation for developing adaptive, user-centric, and friction-aware biometric authentication systems.

Financial information systems in higher education institutions manage highly sensitive assets, including tuition payments, scholarships, payroll, vendor transactions, budgeting, and institutional financial reporting. Although ISO/IEC 27001:2022 provides a risk-based foundation for establishing an Information Security Management System, its implementation in universities is frequently constrained by fragmented governance, limited resources, complex asset environments, inconsistent managerial commitment, cultural resistance, and limited real-time monitoring capability. This study aims to develop an integrated security evaluation model for campus financial information systems by combining ISO/IEC 27001:2022, Zero Trust Architecture, AI-driven threat detection, security maturity assessment, and human-factor analysis. The study adopts a mixed-method sequential explanatory design integrated with Design Science Research. Quantitative stages include asset identification, risk scoring, ISO 27001 control gap analysis, maturity assessment, Zero Trust readiness assessment, and AI-driven detection readiness assessment. Qualitative stages include document analysis, semi-structured interviews, observation, expert judgment, and thematic analysis to examine organizational, cultural, and behavioral factors influencing security control effectiveness. The proposed outcome is the HEFIS-ISMS Model, an integrated framework consisting of seven layers: ISO 27001 control compliance, risk-based asset protection, security maturity, human and organizational factors, Zero Trust readiness, AI-driven detection readiness, and improvement roadmap. The model is expected to address the static and compliance-oriented limitations of conventional ISO 27001 assessments by introducing adaptive access control, continuous monitoring, anomaly detection readiness, and phased implementation guidance. The study contributes theoretically to cybersecurity governance in higher education and practically to risk-prioritized security improvement for resource-constrained universities.

This study aims to analyze the state of cybersecurity and examine the implementation of risk management strategies at the Communication and Informatics Office of North Sumatra Province. The study employed a qualitative approach with descriptive methods, using interviews and documentation as data collection techniques. Interviews were conducted with three informants directly involved in cybersecurity management. The results indicate that the implementation of cybersecurity risk management has been carried out systematically through the stages of risk identification, analysis, evaluation, handling, and monitoring, supported by the use of a risk register and the Plan, Do, Check, Act (PDCA) cycle approach within the Information Security Management System framework. Risk assessments are conducted based on the level of impact and likelihood to determine priority for handling. The implemented mitigation strategies include risk control, avoidance, and transfer, with a focus on high-level risks. However, implementation still faces obstacles such as limited human resources, suboptimal internal policies, and a lack of support systems. Therefore, strengthening is needed through improving human resource competency, refining policies, and ongoing monitoring and evaluation to enhance the effectiveness of risk management and cybersecurity resilience.

The rapid advancement of digital technology has fundamentally altered the landscape of public governance, compelling local governments to adapt and embrace digital transformation. This study investigates the challenges and opportunities faced by local governments in Indonesia in implementing digital governance transformation within the framework of Society 5.0. Using a systematic literature review and case study methodology, this research analyzes governance transformation policies, institutional readiness, digital infrastructure, and human resource capacity across selected Indonesian regional governments. The findings reveal that while significant opportunities exist including enhanced public service delivery, improved transparency, citizen participation, and inter-agency coordination substantial challenges persist in digital infrastructure disparities, limited human resource capacity, regulatory ambiguity, and cybersecurity vulnerabilities. The study identifies five critical success factors for effective digital governance transformation: strong political commitment, adequate digital infrastructure investment, comprehensive human resource development, adaptive regulatory frameworks, and inclusive citizen engagement mechanisms. This research contributes to the theoretical discourse on e-government and digital governance in the context of developing countries, while offering practical policy recommendations for local governments navigating the transition to Society 5.0. The implications extend to policymakers, practitioners, and scholars engaged in public administration reform in the digital age.

The increasing sophistication of malware has rendered traditional signature-based detection methods insufficient, necessitating behavior-driven and adaptive analytical frameworks. This study presents a sequential deep learning framework that models system-level API call sequences as structured linguistic representations for behavioral malware detection. Unlike conventional comparative studies, this work systematically evaluates recurrent and attention-based architectures under controlled experimental conditions, with a particular focus on generalization performance and overfitting mitigation. Two neural architectures, a Long Short-Term Memory (LSTM) network and a Transformer-based attention model, are trained on publicly available API call sequence data for binary classification of malicious and benign executables. Beyond standard accuracy metrics, the study further examines model stability, convergence behavior, and the impact of long-range dependency modeling on detection robustness. Experimental results demonstrate that the Transformer architecture achieves superior performance, attaining 95.54% classification accuracy and consistent improvements in precision, recall, and F1-score, indicating a stronger ability to capture complex behavioral dependencies. These findings highlight the effectiveness of attention mechanisms in behavioral malware modeling and provide empirical evidence that NLP-inspired architectures offer a robust and scalable approach for real-world cybersecurity applications.

Explainable artificial intelligence (XAI) has become a critical requirement in cybersecurity due to the high-stakes nature of security decision-making and the limitations of black-box learning models. This study investigates the construction of an explainable cybersecurity knowledge representation by leveraging standardized terminology from the NIST cybersecurity glossary. The primary problem addressed is the lack of transparent and semantically grounded reasoning mechanisms in existing AI-driven cybersecurity systems, which limits trust, accountability, and analyst adoption. To address this challenge, we propose a NIST-based semantic knowledge graph that embeds explainability directly into its ontology structure and reasoning process. The proposed framework systematically extracts definitional entities and relations from NIST glossary entries to construct a domain ontology and a multi-relational knowledge graph. A rule-based semantic relation extraction method is employed to ensure faithful, interpretable, and reproducible reasoning paths. The resulting knowledge graph contains over 3,000 cybersecurity concepts and approximately 27,000 semantic relations, covering hierarchical, associative, dependency, and mitigation semantics. Experimental evaluation demonstrates that the proposed approach achieves a high level of explainability, with 92.4% of reasoning outcomes being fully traceable and only 1.4% classified as non-traceable. Most explainable reasoning paths are limited to two or three hops, indicating an effective balance between inferential depth and human interpretability. Structural analysis further confirms the presence of meaningful hub concepts that support multi-hop semantic inference. These results confirm that ontology-driven, standard-based knowledge graphs provide a robust foundation for explainable cybersecurity intelligence. The study concludes that explainability-by-design, grounded in authoritative standards, offers a viable and trustworthy alternative to opaque AI models for cybersecurity applications.

Inconsistent terminology across cybersecurity frameworks undermines global governance and interoperability. The National Institute of Standards and Technology Cybersecurity Framework (NIST CSF 2.0) and ISO/IEC 27001:2022 share similar objectives but diverge semantically in defining risk, control, and resilience. This semantic gap causes difficulties in compliance mapping and automated policy translation. Research Objectives: This study aims to analyze the semantic similarity and divergence between NIST and ISO/IEC 27000 terminologies, identify conceptual structures influencing interoperability, and propose an AI-assisted foundation for harmonizing cybersecurity language globally. Methodology: A mixed-method semantic comparative design integrates Natural Language Processing (NLP) and ontology mapping. Using the nist_glossary.csv dataset and ISO vocabularies, terms were normalized and analyzed via cosine similarity using sentence-transformer embeddings. Ontological alignment was visualized through the Semantic Threat Graph (STG) and validated by certified experts using Cohen’s Kappa reliability tests. Results: From 672 term pairs, results show 40.9% high semantic equivalence, 38.8% partial overlap, and 20.3% semantic divergence. Strongest alignment appears in “Protect” and “Identify” domains, while divergences occur in governance and recovery-related terms. Ontology mapping revealed three conceptual clusters—Risk Governance, Technical Safeguards, and Organizational Readiness. Conclusions: Findings confirm a 79.7% total semantic alignment, indicating strong potential for harmonizing global cybersecurity standards. The study contributes an empirical model combining computational linguistics and AI-based ontology mapping to establish semantic interoperability, enabling unified cybersecurity governance and AI-driven compliance automation. Keywords: Semantic Interoperability; Ontology Mapping; Cybersecurity Frameworks; Terminology Alignment; AI Harmonization

The accelerating digitalization of vessel navigation and communication systems has introduced unprecedented cybersecurity vulnerabilities into maritime operations, threatening the safety, security, and commercial continuity of global and Indonesian shipping. This study investigates maritime cybersecurity risk management practices aboard Indonesian merchant vessels, proposing a cyber-resilience maturity model specifically designed for bridge system protection and crew cyber-awareness development. Employing a qualitative research design with thematic analysis, the study engaged maritime cybersecurity specialists, vessel masters, shipping company IT security officers, and maritime safety academics as primary respondents. Findings demonstrate an overall cybersecurity readiness composite score of 4.09 out of 5.00, with crew cyber-awareness and incident response protocol deficiencies identified as the most critical vulnerability domains. The research demonstrates that Indonesian merchant vessels face elevated cybersecurity exposure attributable to inadequate cyber hygiene protocols, insufficient crew training, and the absence of vessel-specific cyber risk management frameworks. The study contributes a cyber-resilience maturity model and audit framework adaptable for integration into STIP Jakarta's maritime safety training curriculum and industry application across Indonesian shipping companies.

This study presents a Graph-Augmented Isolation Forest (GAIF), an unsupervised anomaly-detection framework for analyzing mobile user behavior. The proposed framework represents users and behavioral attributes as a user–feature bipartite graph, enabling the capture of relational dependencies that are not explicitly modeled in conventional vector-based approaches. Low-dimensional user representations are learned through Node2Vec and Graph Sample and Aggregate (GraphSAGE), and the resulting embeddings are subsequently processed by an Isolation Forest to produce anomaly scores. Experiments are conducted on a Mobile Device Usage and User Behavior dataset comprising 700 user profiles derived from application-level behavioral indicators. The dataset is treated as a behavioral abstraction rather than as a malware classification benchmark. A consistent 80:20 stratified train–test split is employed, with all learning-capable operations restricted to the training data to mitigate information leakage. Detection performance is evaluated post hoc using precision, recall, F1-score, and area under the curve (AUC) metrics. Under the evaluated setting, GAIF achieves an F1-score of 0.94 and an AUC of 0.97, demonstrating improved anomaly detection effectiveness relative to representative unsupervised baseline methods. These results are obtained on a static, proxy dataset and should not be interpreted as evidence of real-time deployment capability. Model interpretability is supported through post-hoc Uniform Manifold Approximation and Projection (UMAP) visualizations of the learned embeddings, providing structural insights into anomalous user behavior. Overall, the findings indicate that integrating graph-based representation learning with isolation-based anomaly scoring constitutes a computationally efficient approach for unsupervised mobile user behavior anomaly detection within the scope of this study.

The increasing complexity and scale of modern network traffic driven by IoT and cloud-based infrastructures have made accurate intrusion detection a critical challenge. Conventional network intrusion detection systems (NIDS) and many deep learning–based approaches struggle to reliably detect minority and stealthy attacks due to severe class imbalance and limited discrimination of subtle traffic patterns. To address these limitations, this study proposes a hybrid CNN–RBF–Attention framework for network intrusion detection. The proposed model integrates three complementary components: (i) a convolutional neural network for hierarchical feature extraction from network flow data, (ii) a radial basis function (RBF) network for localized nonlinear classification using prototype-based decision regions, and (iii) an attention mechanism that adaptively weights RBF activations to emphasize discriminative traffic patterns. SMOTE is applied exclusively to the training data to mitigate class imbalance. The framework is evaluated on the widely used CICIDS2017 and CICIDS2018 benchmark datasets in both binary and multiclass settings, using recall, precision, F1-score, confusion matrices, and ROC analysis. Experimental results demonstrate that the proposed hybrid model consistently outperforms standalone CNN and RBF baselines, particularly in terms of recall and F1-score. On the CICIDS2018 dataset, the model achieves 99.81% accuracy and 99.81% F1-score in binary classification, and 99.54% accuracy and 99.54% F1-score in multiclass classification. On CICIDS2017, it achieves 98.12% accuracy and 98.12% F1-score in binary classification, and 98.92% accuracy and 98.92% F1-score in multiclass classification. Confusion matrix and ROC analyses further show strong class separability and reliable performance in low–false-positive-rate regions, which is critical for real-world IDS deployment. These results confirm that combining deep hierarchical feature learning, localized prototype-based classification, and attention-guided refinement yields a robust, operationally reliable intrusion detection framework for highly imbalanced network environments.

This study presents a comprehensive comparative analysis of four traditional machine learning algorithms Decision Tree, Random Forest, K-Nearest Neighbors, and Support Vector Machine for Android malware detection using the preprocessed TUANDROMD dataset comprising 4,465 instances and 241 features representing both static and dynamic application characteristics. Motivated by the limitations of conventional signature-based and hybrid detection methods, especially in managing imbalanced datasets and detecting emerging malware variants, the study employed SMOTE to ensure balanced training data and fair model evaluation. The dataset was divided into 80% training and 20% testing subsets, and models were assessed using key performance metrics including accuracy, precision, recall, F1-score, and ROC AUC. The findings revealed that the proposed Random Forest model outperformed the other classifiers, achieving an accuracy of 0.993, precision of 0.992, recall of 1.000, F1-score of 0.996, and a near-perfect ROC AUC of 0.9998 surpassing state-of-the-art approaches. These results affirm the superior predictive capability, consistency, and robustness of the Random Forest algorithm in Android malware detection. The study concludes that base models, when integrated with class-balancing techniques, provide reliable and efficient malware detection across imbalanced datasets. For future research, the study recommends exploring advanced hybrid or ensemble frameworks that integrate Random Forest with deep learning architectures or other meta-heuristic optimization techniques to further enhance detection accuracy, adaptability, and resilience against rapidly evolving Android malware threats.