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All Journal African Multidisciplinary Journal of Sciences and Artificial Intelligence Kwaghe International Journal of Sciences and Technology
Sampson Agada
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Environmental Science Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Social Sciences

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Detection of Malware Attacks in Medical Mechatronics Distribution System Using Support Vector Machine Bartholomew Idoko; Okoro Denis Isah; Sampson Agada; Samuel Owoicho Olofu
African Multidisciplinary Journal of Sciences and Artificial Intelligence Vol 2 No 3 (2025): African Multidisciplinary Journal of Sciences and Artificial Intelligence
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/amjsai.v2i3.7231

Abstract

This study examines the cybersecurity challenges and solutions associated with medical mechatronics devices, which increasingly operate autonomously through advances in robotics, artificial intelligence (AI), and machine-to-machine communication. As the number of interconnected devices grows—from smart grids and home appliances to medical equipment and sensor–actuator testers—ensuring secure and trustworthy communication becomes critical. A sustainable defensive strategy for medical mechatronics requires robust systems capable of malware analysis and detection, informed by an understanding of cyber-attack stages such as reconnaissance, weaponization, delivery, exploitation, installation, and command and control. Traditional malware detection systems struggle with obfuscated malware, making AI and machine learning (ML) more effective tools for accurate detection and classification. This research proposes the use of a Support Vector Machine (SVM) model with a novel metric to enhance malware detection in medical mechatronics devices, thereby strengthening confidentiality, integrity, availability, and digital trust. The proposed SVM-based approach was compared with established SVM algorithms using a real dataset from medical mechatronics distribution systems across federal medical centers in Nigeria. Findings demonstrate the potential of the model to improve malware detection accuracy and compliance with digital sovereignty standards, offering practical insights for enhancing cybersecurity in critical healthcare technologies.
Cryptographic System for Mobile Application (Automated Resume Builder) Bartholomew Idoko; Sampson Agada; Okoro Denis Isah; Chika Patricia Bossah
Kwaghe International Journal of Sciences and Technology Vol 2 No 3 (2025): Kwaghe International Journal of Sciences and Technology
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/kijst.v2i3.7228

Abstract

This paper presents a secure and user-friendly approach for developing an automated mobile application system, using a resume builder as a case study. The proposed system automates the construction of resumes by utilizing applicants’ information as input, allowing users to create, edit, delete, read, and save resumes in PDF format, supported by login and signup via OTP verification. To enhance security, the study introduces a two-factor authentication (TFA) scheme that integrates a cryptographic-compatible device and a password, offering stronger protection against risks such as communication breaches, device or server vulnerabilities, and offline or online credential attacks. The TFA is implemented through shared access signature (SAS) message authentication or other PIN-based authentication methods. The system architecture incorporates an enhanced cryptographic framework adaptable to various password-based client–server authentication protocols, reducing reliance on less secure single-layer password systems. Data encryption is handled using the Advanced Encryption Standard (AES), chosen over 3DES for its superior processing efficiency, while the Message-Digest Method (MD5) algorithm is used to hash user-defined encryption keywords. All server-side data, including encryption keys, remain encrypted, ensuring that unauthorized access yields no advantage. By enabling users to encrypt and decrypt data with AES and securing encryption keys via MD5 hashing, the system improves both privacy and security in mobile applications. The study contributes to secure software design by demonstrating how cryptographic methods can be modularly integrated into mobile systems, addressing the cybersecurity gaps of conventional job search and resume platforms.
Co-Authors Bartholomew Idoko Chika Patricia Bossah Okoro Denis Isah Samuel Owoicho Olofu