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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.  

The advancement of technology and information, which coincides with digitalization alongside the development of Artificial Intelligence (AI), has colored human social life around the world. Currently, cadets of the Naval Academy often use gadgets/digital tools to access information or other media. With this, there are emerging threats that are very dangerous for individuals and the state. An currently prevalent case is Cyber Crime Identity Theft. Cyber crime identity theft refers to the crime of stealing someone's personal information online for the purpose of fraud or financial gain by stealing personal data such as names, identity numbers, and financial information, to carry out illegal transactions or obtain illegal benefits, which can lead to financial loss and damage the victim's reputation. Indonesia still has a very high number of cases of cyber crime identity theft, but it should be preventable by fostering. the cyber security awareness of cadets at the Naval Academy of the Indonesian National Armed Forces. The conclusion of this research is that by strengthening modern technological infrastructure, both equipment and administrative services, developing modern software and hardware with strong security systems, and tightening regulations regarding access to users' personal data in cyber services, supported by strengthening cyber security institutions, it can enhance the cyber security awareness of cadets at the Naval Academy of the Indonesian National Armed Forces.

Digital democracy and e-governance have emerged as transformative instruments for promoting transparency, accountability, and efficient public administration in India. This study examines the role of digital governance initiatives in strengthening democratic participation and reducing corruption through technology-driven governance mechanisms. Using a qualitative and doctrinal research methodology based on secondary sources, the study analyses major initiatives such as Digital India, Aadhaar, DigiLocker, BharatNet, BHIM-UPI, and MyGov. The findings demonstrate that digital governance has improved accessibility to public services, enhanced administrative efficiency, and minimized bureaucratic corruption through automation and electronic monitoring systems. Furthermore, digital platforms have expanded citizen participation in democratic processes by facilitating communication between governmental institutions and the public. However, challenges such as digital inequality, cyber-security risks, infrastructural limitations, and privacy concerns continue to affect the effectiveness of digital democracy in India

The proliferation of social media interactions frequently results in an increased risk of cyberbullying. This study aims to evaluate the impact of digital literacy, digital security, and digital communication on cyberbullying prevention among Generation Z TikTok users. Employing an explanatory quantitative approach, data were collected through online questionnaires distributed to Generation Z students in the Information Systems Study Program at Universitas Internasional Batam. The respondent data were then analyzed using the Partial Least Square-Structural Equation Modeling (PLS-SEM) technique. The analytical results reveal that digital literacy does not significantly affect cyberbullying prevention. Conversely, both digital security and digital communication demonstrate a significant impact on cyberbullying prevention. Among the investigated variables, digital communication exerts the most dominant influence on preventing cyberbullying. These findings suggest that preventing cyberbullying requires more than merely digital knowledge; it heavily relies on the implementation of digital security measures and the capacity for ethical communication in digital environments. Ultimately, this research is expected to serve as a valuable reference for formulating effective cyberbullying mitigation strategies for Generation Z across social media platforms.

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 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

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.

This study aims to analyze the Cambodian government's efforts to address human trafficking from a human security perspective. It employs a qualitative descriptive approach, utilizing secondary data from relevant journals, reports, and academic publications. The findings indicate that human trafficking in Cambodia has evolved into a hub for digital-based fraud linked to transnational cybercrime networks. Victims face not only labor exploitation but are also coerced into participating in online fraudulent activities, such as romance scams. This surge in human trafficking is driven by weak law enforcement, high levels of corruption, low public digital literacy, and the rapid expansion of the online gambling industry and special economic zones, which serve as operational bases for criminal activities. The Cambodian government has implemented various measures regarding prevention, protection, and law enforcement through regulations, international cooperation, victim rescue operations, and anti-fraud campaigns. However, the effectiveness of these policies remains limited due to the state's constrained capacity and the ability of criminal networks to continuously adapt to advancements in digital technology. The study concludes that addressing human trafficking in Cambodia requires strengthened governance, more intensive international cooperation, and improved public digital literacy.

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 digitalization in Indonesia, reaching 180 million active social media users, has not been accompanied by adequate security system resilience, thereby triggering massive data breach risks. This study aims to analyze the privacy navigation mechanisms of the digital society as an instrument for mitigating information leaks. The method used is descriptive qualitative with a literature study (library research) approach, which examines primary and secondary literature related to regulations, digital behavior, and user psychological factors. The research findings indicate that privacy navigation in the digital era has not operated optimally due to the dominance of social existence needs, which triggers the privacy paradox phenomenon. Although users possess knowledge regarding cyber risks, the desire for social validation through self-disclosure often overrides technical protection logic. The practice of using secondary accounts (second accounts) was found to be a form of manual navigation, yet its effectiveness remains dependent on individual digital literacy. The implications of this research emphasize that mitigating information leaks requires the integration of critical user awareness, platform governance transparency, and consistent law enforcement through the PDP Law. Digital awareness must transform into reflexive protective behavior to maintain informational sovereignty in cyberspace.  

 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.

The development of digital technology and artificial intelligence (AI) has increased youth interaction in cyberspace, while also elevating the risk of digital crimes, both as perpetrators and victims. This community service program AIms to strengthen legal awareness and AI-based self-protection skills among students of SMKN 1 Kaligondang, \Purbalingga Regency. The implementation methods include interactive workshops, digital security trAIning, case simulations, and the use of a mini AI assistant as a preventive educational tool. Evaluation was conducted through pre-test and post-test assessments, participatory observation, and participant reflection. The results indicate an improvement in participants’ understanding of digital law, particularly regarding the provisions of the Law on Electronic Information and Transactions and its relevance to the Indonesian Criminal Code. In addition, participants’ technical skills in securing digital accounts, identifying phishing attempts, and responding to cyber risks have also improved. The use of AI has proven to enhance participation and contextual understanding. Overall, the program is effective in fostering legal awareness, improving digital protection capabilities, and shaping responsible behavior in cyberspace.

This article discusses innovations in cash waqf management through the integration of digital technology with the SatuWakaf.id platform developed by the Indonesian Waqf Board (BWI). The urgency of this research is driven by the need for a transformation in waqf governance to optimize the potential of the Sharia economy in the era of disruption. This study employs a literature review method with a qualitative approach to examine the role of digitalization in enhancing efficiency, transparency, and public participation in waqf. The findings indicate that digital waqf provides ease of access, transaction flexibility, and transparency through a real-time reporting system that is accountably accessible to donors (wakif). Furthermore, the integration of technologies such as QRIS, e-wallets, and internet-based systems effectively addresses various constraints of conventional waqf management, including limited reach and complex administration. However, significant challenges remain, such as low public literacy, limited technological competence among waqf managers (nazir), and cyber security risks surrounding digital transactions. Consequently, the optimization of digital waqf requires strong synergy between human resource development, adaptive regulatory strengthening, and the development of inclusive technological infrastructure to ensure that national waqf potential is fully utilized for the welfare of the community.

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.

This study examines the effectiveness of legal protection for victims of cyberbullying in Indonesia from a victimological perspective. The rapid development of information technology and social media has led to an increase in cyberbullying cases, which significantly impact victims psychologically, socially, and emotionally. However, existing legal protections are often considered inadequate in addressing the needs of victims. This research aims to analyze the existing legal framework, evaluate its effectiveness, and assess the position of victims within the criminal justice system. The method used is normative legal research with statutory, conceptual, and victimological approaches, relying on secondary data from relevant legal literature and scientific journals. The findings indicate that although regulations such as the Electronic Information and Transactions Law (ITE Law) provide a legal basis, their implementation remains ineffective due to weak law enforcement, lack of victim-oriented approaches, and limited awareness of victims’ rights. Therefore, strengthening victim protection through a victimological approach, legal reform, and the application of restorative justice is necessary to ensure justice and recovery for victims.           

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.

Phishing is a form of cybercrime that has experienced a significant increase in frequency within Indonesia. This fraudulent practice aims to deceive victims into surrendering personal data or sensitive financial information by impersonating trusted institutions. Such crimes result in substantial losses for both individuals and the business sector, particularly concerning personal data protection and digital transaction security. This research aims to analyze the legal regulations and the role of supervisory institutions in addressing phishing threats in Indonesia using a normative legal research method. The legal analysis encompasses the implementation of the Electronic Information and Transactions Law (UU ITE), specifically Article 28, paragraph (1), and the Personal Data Protection Law (UU No. 27 of 2022), which serves as the primary foundation for privacy rights. Furthermore, this study examines the Consumer Protection Law and the Indonesian Criminal Code (KUHP) as enforcement instruments. The strategic roles of the Financial Services Authority (OJK) and Bank Indonesia (BI) are also discussed in the context of risk mitigation within the financial sector. The findings indicate that law enforcement effectiveness is still hindered by low digital literacy, limited forensic technology infrastructure, and jurisdictional challenges in tracking cross-border perpetrators. Consequently, a synergy between regulatory strengthening, international collaboration, and massive public education is required to comprehensively suppress these cybercriminal activities.

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.