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All Journal JOIV : International Journal on Informatics Visualization Journal La Multiapp
Gaata, Methaq Talib
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering

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Enhancing the Security of Information Systems Using Iot Technology Gaata, Methaq Talib; Mohialden, Yasmin Makki; Mahmood Hussien , Nadia
Journal La Multiapp Vol. 5 No. 4 (2024): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v5i4.1308

Abstract

Psychiatric patient information system that is used in most mental health clinics is very important in dealing with patient records. However, the safety of such systems is a major issue since information being processed in such systems is often sensitive. This paper offers a new way of boosting the security of the Mentcare information system via the incorporation of IoT technology. The following figure demonstrates the components of the proposed security framework of the system which uses highly secure password generation algorithms that enable the system to generate passwords of different levels of complexities depending on the user’s preference. Such improvements guarantee exclusive safeguard mechanisms against illegitimate access since IoT provides a way of passing secure passwords to the right individuals in real-time. That has resulted in the overall decreases in hacking attempts by the unauthorized access and enhanced the encryptions that meet GDPR and HIPAA standards and practices fully integrated with IoT technology. Also, general enhancements have been made on Mentcare system with regard to the ease and speed in generating password, system response time and user satisfaction. In light of these findings, this study reaffirms the need to have IoT-advanced security protocols for medical information systems especially in mental health care where patients’ information needs to be well protected. The conclusions prove that in addition to increasing security, the proposed system optimizes the process of its functioning, which confirms that it is necessary to apply it to protect the health care information.
Voice-Authentication Model Based on Deep Learning for Cloud Environment Hachim, Ethar Abdul Wahhab; Gaata, Methaq Talib; Abbas, Thekra
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1303

Abstract

Cloud computing is becoming an essential technology for many organizations that are dynamically scalable and employ virtualized resources as a service done over the Internet. The security and privacy of the data stored in the cloud is cloud providers' main target. Every person wants to keep his data safe and store it in a secure place. The user considers cloud storage the best option to keep his data confidential without losing it. Authentication in the trusted cloud environment allows making knowledgeable authorization decisions for access to the protected individual's data. Voice authentication, also known as voice biometrics, depends on an individual's unique voice patterns for identification to access personal and sensitive data. The essential principle for voice authentication is that every person's voice differs in tone, pitch, and volume, which is adequate to make it uniquely distinguishable. This paper uses voice metric as an identifier to determine the authorized customers that can access the data in a cloud environment without risk. The Convolution Neural Network (CNN) architecture is proposed for identifying and classifying authorized and unauthorized people based on voice features. In addition, the 3DES algorithm is used to protect the voice features during the transfer between the client and cloud sides. In the testing, the experimental results of the proposed model achieve a high level of accuracy, reaching about 98%, and encryption efficiency metrics prove the proposed model's robustness against intended attacks to obtain the data.
Co-Authors Abbas, Thekra Hachim, Ethar Abdul Wahhab Mahmood Hussien , Nadia Mohialden, Yasmin Makki