Cybersecurity and Innovative Technology Journal
Cybersecurity and Innovative Technology Journal is a peer-reviewed, open-access journal that publishes high quality articles both the theoretical foundations and practical applications of cybersecurity and innovative technology worldwide. We welcome original research articles and reviews that contribute to the understanding and application of cybersecurity and innovative technology. Our scope covers a wide range of cybersecurity and innovative technology, including but not limited to: Cybersecurity and Applications Innovative Technology and Applications Artificial Intelligence and Applications. 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. Cybersecurity and Innovative Technology Journal has been indexed by Cybersecurity and Innovative Technology Journal is cited by articles in Scopus database at least 66 times since published in September 2023 available online on <a href="https://www.scopus.com/results/results.uri?st1=Citj&st2=&s=%28SRCTITLE%28Citj%29+OR+SRCTITLE%28Cybersecur+Innovative+Technol+J%29+OR+SRCTITLE%28cybersecur+AND+innov+AND+technol+AND+j%29+OR+SRCTITLE%28cybersecurity+AND+innovative+AND+technology+AND+journal%29+OR+SRCTITLE%28cybersecurity+AND+innov.+AND+technol.+AND+j.%29%29&limit=10&origin=resultslist&sort=plf-f&src=dm&sot=b&sdt=cl&sessionSearchId=8d3cf53c8039e0392c47fdca2f6d2fea&cluster=scoexactsrctitle%2C%22Citj+as+A+Terminal%22%2Cf%2C%22Independent+Citj+Website+Sites+and+Networks%22%2Cf%2C%22Blockchain+in+Cybersecurity+Jetir+Journal+of+Emerging+Technologies+and+Innovative+Research%22%2Cf">https://scopus.com.
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Artificial Intelligence Voice Cloning in Healthcare Cybersecurity: Challenges, Opportunities, and Protective Strategies
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.
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The Connection Between Hackers and Their Affinity Towards Healthcare
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
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
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
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.