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Cybersecurity and Innovative Technology Journal
Published by Gemilang Maju Publikasi Ilmiah
ISSN : -     EISSN : 30256682     DOI : http://dx.doi.org/10.53889/citj.v2i1.338
Core Subject : Science,
Computer Science & IT
Cybersecurity and Innovative Technology Journal is a peer-reviewed journal that covers research publications and review articles in the field of cybersecurity and innovative technology. Cybersecurity and Innovative Technology Journal is published by Gemilang Maju Publikasi Ilmiah (GMPI). Cybersecurity and Innovative Technology Journal has become a Crossref member since year 2023 with prefix 10.53889. Therefore all articles published by Cybersecurity and Innovative Technology Journal will have uniques DOI numbers since Vol.1, No.1, September 2023. Since Vol.1, No.1, September 2023. Cybersecurity and Innovative Technology Journal uses Anti-Plagiarism Software "Turnitin" to check the authenticity article.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 30 Documents
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Artificial Intelligence Voice Cloning in Healthcare Cybersecurity: Challenges, Opportunities, and Protective Strategies Triplett, William J.
Cybersecurity and Innovative Technology Journal Vol 3, No 2 (2025)
Publisher : Gemilang Maju Publikasi Ilmiah (GMPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53889/citj.v3i2.836

Abstract

AI voice cloning has been one of the most transformative technologies in the health sector, improving patient-provider communication and enhancing operations in telehealth and remote monitoring. However, it comes with its own unique cybersecurity risks, including identity theft and unauthorized access. This paper focuses on the risks of cybersecurity threats in healthcare with relation to the use of AI voice cloning and assesses the efficiencies of current protective measures before proposing additional recommendations for enhanced security for data in the health sector. Based on mixed-method research, the study includes a systematic literature review and interviews with HCI and IT professionals, cybersecurity experts, and AI technology developers. It also accents current threats as well as possible solutions to the emerging vulnerabilities. The study shows that even though the use of AI for voice cloning has massive benefits, it creates significant security issues. These are: the use of voice phasing, unauthorized operations, and falsification of patient identity. The study calls for integrated security measures to fashion out secure AI systems, embrace enhanced modes of authentication, and conduct AI system check-ups frequently to avoid compromised voice cloning technology in healthcare.
The Connection Between Hackers and Their Affinity Towards Healthcare McBride, Maurice L
Cybersecurity and Innovative Technology Journal Vol 3, No 2 (2025)
Publisher : Gemilang Maju Publikasi Ilmiah (GMPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53889/citj.v3i2.837

Abstract

Cybercriminals target the healthcare sector because patient data can be traded illegally. These include ransomware attacks and medical record theft. This article analyzes the internal factors that make healthcare firms vulnerable to cybercrime. It also examines their complicated tactics for entering and profiting from these sectors. This paper examines healthcare cybersecurity case studies and current research. The goal is to help readers understand the main challenges of fighting different cyberattacks. The methodology part includes a thorough literature review and rigorous analysis of data from academic sources, industry publications, and cyber security incident databases. The numbers show the diverse threat actors targeting the healthcare business, their techniques, and the vulnerabilities of healthcare institutions to cyber attacks. The report concludes that healthcare cybersecurity is crucial. The guidelines presented are essential for healthcare cybersecurity.
Considerations for the Safety Analysis of AI-Enable Systems Green, Christopher W
Cybersecurity and Innovative Technology Journal Vol 3, No 2 (2025)
Publisher : Gemilang Maju Publikasi Ilmiah (GMPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53889/citj.v3i2.670

Abstract

This study explored the applicability of hazard analysis techniques to Artificial Intelligence/Machine Learning AI-enabled systems, a growing area of concern in safety-critical domains. The study evaluates 127 hazard analysis techniques described in the System Safety Society’s System Safety Analysis Handbook (1997) for their relevance to the unique challenges posed by AI-enabled systems. A qualitative criteria-based assessment framework was employed to systematically analyze each technique against key AI-specific considerations, including complexity management, human-AI interaction, dynamic and adaptive behavior, software-centric focus, probabilistic and uncertainty handling, and iterative development compatibility. The evaluation process involved defining criteria to address AI/ML systems' distinctive characteristics, assessing each method's applicability, and ranking techniques based on their alignment with AI-related challenges. Findings indicate that Fault Tree Analysis (FTA) and Human Reliability Analysis (HRA) are highly relevant for performing safety on AI-enabled systems. Other techniques, such as What-If Analysis, require adaptation to address emergent behaviors. This study provides a framework for selecting and tailoring hazard analysis methods for AI-enabled systems, contributing to developing robust safety assurance practices in an increasingly intelligent and autonomous era.
Role of Data Centers in Healthcare Technology Triplett, William J.
Cybersecurity and Innovative Technology Journal Vol 3, No 2 (2025)
Publisher : Gemilang Maju Publikasi Ilmiah (GMPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53889/citj.v3i2.838

Abstract

This article examined changes in data centers for healthcare consumers, how they improve efficiency and security, and patient-centered care. Thus, analyzing literature from 2020-2024, it outlines trends, advantages, and disadvantages of using healthcare data centers and explores possibilities of their integration with other technologies, including AI and blockchain. Research indicates that the healthcare sector could not achieve the digital front without data centers, but these centers would demand heavy capital and a robust security system for optimum results. The research points to the fact that there is still a strong need to invest and work on improving the technologies in managing health care data.
System Safety Preliminary Hazard Analysis (PHA) Using Generative Artificial Intelligence Green, Christopher W
Cybersecurity and Innovative Technology Journal Vol 3, No 2 (2025)
Publisher : Gemilang Maju Publikasi Ilmiah (GMPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53889/citj.v3i2.671

Abstract

This study investigated the capability of ChatGPT, an AI-powered generative language model, to perform hazard analysis for complex systems using the ACME Missile System as a case study. Hazard analyses generated by ChatGPT were compared to those detailed in Ericson, Clifton's 2005 publication, Hazard Analysis Techniques for System Safety, focusing on adherence to MIL-STD-882E methodologies. The research addresses general questions regarding the strengths and limitations of ChatGPT in identifying hazards, assessing risks, and proposing mitigation strategies. Through a structured evaluation, the study examines the completeness, accuracy, and alignment of ChatGPT-generated analyses with traditional techniques, identifying areas of strength, such as efficiency and innovative mitigation suggestions, alongside gaps in contextual understanding and methodological consistency. Findings highlight the potential of ChatGPT as a supplementary tool for initial hazard identification, emphasizing the importance of expert validation to ensure reliability in safety-critical applications. This research contributes to understanding AI’s role in system safety engineering and integration into existing hazard analysis frameworks.
Elementary School Students’ Readiness to Adopt Artificial Intelligence (AI)-Based Learning Yulianti, Anggun; Andrijati, Noening; Wijayati, Nanik; Avrilianda, Decky
Cybersecurity and Innovative Technology Journal Vol 4, No 1 (2026)
Publisher : Gemilang Maju Publikasi Ilmiah (GMPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53889/citj.v4i1.879

Abstract

This research will focus on the evaluation of elementary school students' readiness to use AI for their learning process through three significant factors, namely dimensions of readiness, factors affecting readiness, and conditions for infrastructure and ecosystem of schools. The type of this research is a literature review that utilizes qualitative methods. This data collection is done based on ten scientific articles from national and international journals, sourced from the Google Scholar, Scopus, ERIC, and Sinta databases. The data were collected by documentation, and data analysis was done using content analysis and thematic synthesis. From the research findings, it is evident that the readiness for AI integration among primary level pupils entails three related constructs; cognitive (knowledge of AI), affective (attitudes, motivation, self-confidence), and behavioral (interaction skills with AI technologies). While internal variables (teacher attitudes, TPACK) strongly positively impact AI integration (β = 0.791), external variables (government policies, technological infrastructure, community involvement) indirectly impact AI usage via internal variables (β = 0.217). Less than a third of teachers (28%) currently adopt AI in teaching, whereas the majority (82%) stick to conventional instructional techniques. These results indicate that the readiness of students will not take place without ensuring the readiness of teachers and that both need to work in harmony with each other for internal capacity building and equal external support. The conclusion drawn from this study is that the level of readiness of elementary school students to adopt learning based on artificial intelligence has only started and that they are not consistent yet. The application of AI in elementary schools largely relies on enhancing teacher competencies, equal infrastructure, and developing policies that consider ethics, privacy, and social justice. More studies should be conducted concerning rural areas and tools to measure readiness in Indonesia.
The Integration of Digital Interactive Whiteboards in Elementary School Education: A Literature Review Based on the TPACK Framework Saputri, Kiki Niken; Anggraini, Ade Eka; Nusantara, Toto; Pristiani, Riska; Arifin, Slamet
Cybersecurity and Innovative Technology Journal Vol 4, No 1 (2026)
Publisher : Gemilang Maju Publikasi Ilmiah (GMPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53889/citj.v4i1.880

Abstract

This study seeks to analyze and synthesize the outcomes of previous studies about the implementation of PID in elementary education using the TPACK framework as a framework for analysis. This study is a review of scientific literature that analyzes ten scientific articles found in national and international scientific journals accessed via the Google Scholar, ERIC, and ScienceDirect databases. The technique utilized in analyzing the data involves collecting, coding, organizing, and synthesizing the information derived from the research. Based on the findings of this study, PK and TPK are identified as the most dominant dimensions of TPACK in the process of implementing PID in the classroom setting. On the other hand, TK and TCK were recognized as the weakest elements among the ten articles examined. It can be inferred from these findings that teachers are more at ease discussing instructional matters and integrating technology in their teaching methods, but still have issues in terms of technical proficiency in using devices and integrating technology in the curriculum content. In addition, it was also noted that teachers who used student-centered pedagogy effectively developed transformative TPACK, while teachers who utilized teacher-centered pedagogy failed to do so. Contextual knowledge (CK) has similarly been found to be an essential variable in moderating the effectiveness of PID implementation in various school contexts. In summary, the results obtained in this study prove that the effectiveness of PID implementation in elementary schools is not only contingent upon the presence of technology tools and their use in training sessions, but more importantly, the effective implementation of PID relies on the appropriate combination of all the seven TPACK components, especially TK and TCK, a student-centered instructional model, and careful considerations of the teaching and learning context. Finally, it is recommended that there should be more holistic, collaborative, and sustainable professional development programs for teachers, as well as future studies involving experimental and longitudinal research designs to investigate the effects of different TPACK training approaches in elementary schools.
The Effect of Using BLAST (Basic Local Alignment Search Tool) on the Conceptual Understanding of Students on Evolutionary Concepts Allisa, Siti Nur; Sari, Indah Juwita; Rifqiawati, Ika; Ratnasari, Dwi
Cybersecurity and Innovative Technology Journal Vol 4, No 1 (2026)
Publisher : Gemilang Maju Publikasi Ilmiah (GMPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53889/citj.v4i1.876

Abstract

This study aimed to determine the effect of using the Basic Local Alignment Search Tool (BLAST) on students' learning independence and conceptual understanding of evolution at the senior high school level. The research uses a quantitative approach with a weak experimental, one-group pretest-posttest design. The study population consists of Grade XII Science students at a public senior high school in Tangerang, Indonesia. A random sample of 70 students formed the experimental group. Data collection used non-test methods, such as questionnaires for learning autonomy and essay tests for conceptual understanding. The average learning independence score was 79.1, indicating high independence. The average concept understanding score was 57.9, categorized as sufficient. Data analysis for learning independence used the Paired-Samples Test, while concept understanding was analyzed with the nonparametric Wilcoxon test. Results showed the use of BLAST had a significant effect on improving students' learning independence and conceptual understanding, with a p-value of 0.001 0.05. 
A Comparison of UAF and SysML-Based DODAF Implementations for Cybersecurity Architecture Modeling Green, Christopher
Cybersecurity and Innovative Technology Journal Vol 4, No 1 (2026)
Publisher : Gemilang Maju Publikasi Ilmiah (GMPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53889/citj.v4i1.854

Abstract

IoT systems with constrained resources are moving towards energy harvesting to ensure sustainable and autonomous operations in resource-limited environments. Yet, incorporating energy harvesting in such a system involves intricate dependencies between energy production, energy storage capabilities, system operation, security controls, and availability considerations at a mission level. Current practices focus on analyzing the mentioned dependencies for individual components/subsystems without accounting for potential cross-domain effects, thus leaving open room for potential errors in system-level integration. In this paper, we propose an architecture-driven approach for integrating energy harvesting based on the Unified Architecture Framework (UAF). Energy availability is considered from the perspective of a system-level architectural constraint within a framework based on a meta-model, instead of being just a design consideration. Capabilities, operations, resources, security, and standards-related concepts are materialized within a common semantic baseline to enable cross-domain traceability of the dependencies between energy, security, and missions. Variability in the energy produced by the energy harvesters propagates through operations, communications, and security controls up to capabilities realization and availability. This technique takes advantage of the relationships between domains provided by the UAF Domain MetaModel to achieve structured dependency propagation and systematic trade-space analysis between domains. It is clear from the findings that integrating energy and cybersecurity elements into a single architectural framework helps minimize fragmentation in integration, enhances system-level reasoning, and facilitates energy-conscious co-design of operations and security functions.
Architecture-Driven Integration of Energy Harvesting in Resource-Constrained IoT Systems Using the Unified Architecture Framework (UAF) Green, Christopher
Cybersecurity and Innovative Technology Journal Vol 4, No 1 (2026)
Publisher : Gemilang Maju Publikasi Ilmiah (GMPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53889/citj.v4i1.856

Abstract

As the use of resource constrained IoT becomes more prevalent, it relies more often on energy harvesting capabilities for sustaining autonomous operations in uncertain power environments. However, incorporating energy harvesting functionality poses challenges as a result of introducing strong interdependencies between energy generation, storage, operational behavior, security measures, and mission availability. Current methods for addressing the problem usually deal with these aspects on a component or subsystem level and therefore lack cross-domain interaction visibility and understanding, which results in an increased probability of integration errors. In this paper, we propose the architecture driven approach for energy harvesting integration by means of the Unified Architecture Framework (UAF). Instead of being considered separately, energy availability will be addressed as an architectural constraint at a system level in a meta-model driven environment. Through the instantiation of capabilities, operations, resources, security, and standards constructs within a semantic baseline, we show the ability to trace energy- security-mission dependencies across multiple domains. This paper describes how harvested energy variance affects system operations, communications, cybersecurity, capabilities, and system availability. The findings suggest that incorporating energy and cyber security principles in an integrated architectural framework can overcome the problem of fragmentation, enhance overall system reasoning, and facilitate energy-conscious joint design of functional and security-related features. The study provides a scalable architectural basis for the design and evaluation of secure IoT systems with energy constraints.

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