Research Article
The Effective Integration of Multi-Factor Authentication (MFA) with Zero Trust Security
Harold Ramcharan*
Issue:
Volume 10, Issue 1, March 2025
Pages:
1-5
Received:
3 February 2025
Accepted:
17 February 2025
Published:
26 February 2025
Abstract: As many organizations face the rise in cyber threats, our digital landscape demands a more vigorous network. This paper explores the effectiveness of integrating Multi-Factor Authentication (MFA) within the popular Zero Trust security model by using a collection of case studies (qualitative analysis) combined with known security breaches (quantitative analysis) as a means of identifying key strategies in determining user authenticity while strengthening trust boundaries. The findings indicate that a comprehensive collaborative approach is necessary when implementing MFA. This approach should integrate real-time enforcement of security policies, leveraging dynamic threat intelligence and situational information to effectively decrease unauthorized access and prevent data breaches. The study concludes with recommendations for implementing MFA as an essential component of Zero Trust architecture. It emphasizes continuous verification while using access control through IT policies for administrators to control user access based on multiple real-time factors. This integration strengthens security postures while maintaining alignment with regulatory compliance standards.
Abstract: As many organizations face the rise in cyber threats, our digital landscape demands a more vigorous network. This paper explores the effectiveness of integrating Multi-Factor Authentication (MFA) within the popular Zero Trust security model by using a collection of case studies (qualitative analysis) combined with known security breaches (quantitat...
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Research Article
An Accurate Three-Step Hybrid Block Method Via Optimization Approach for Solving Mathematical Model of Continuous Fever
Issue:
Volume 10, Issue 1, March 2025
Pages:
6-18
Received:
26 February 2025
Accepted:
8 March 2025
Published:
26 March 2025
DOI:
10.11648/j.ajmcm.20251001.12
Downloads:
Views:
Abstract: Emergence of novel infectious diseases and the resurgence of already known ones and its variants elicit significant concern in our contemporary world. Thus, it is very crucial to utilize all available resources to monitor and control their spread. Most of the epidemiological models developed to study and analyze the characteristics of diseases produced system of differential equations that are coupled in nature, which has become a challenge to researchers to find exact solutions. This work proposes an accurate three-step hybrid block method through optimization approach for solving mathematical models of continuous fever. The techniques of interpolation and collocation were applied to a power series polynomial for the derivation of the method using a three-parameter approximation of the hybrid points. The hybrid points were obtained by minimizing the local truncation error of the main method. The discrete schemes were produced as by-products of the continuous scheme and used to simultaneously solve mathematical models of continuous fever in block mode. The analysis of the basic properties of the method revealed that the schemes are self-starting, convergent, and A-stable. In addition, the analysis of the order of accuracy of the method showed that there is a gain of one order of accuracy in the main scheme where the optimization was carried out. Thereby, enhancing the accuracy of the whole method. The accuracy of the method was ascertained using three numerical examples. Comparison of the numerical results of the new method with those of the existing methods revealed that the newly developed method compares favorably with the existing hybrid block methods. Hence, the new method should be employed for the numerical solution of initial value problems of ordinary differential equations to obtain more accurate results.
Abstract: Emergence of novel infectious diseases and the resurgence of already known ones and its variants elicit significant concern in our contemporary world. Thus, it is very crucial to utilize all available resources to monitor and control their spread. Most of the epidemiological models developed to study and analyze the characteristics of diseases prod...
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Research Article
Comparative Study of Extreme Gradient Boosting (XGBOOST), K-Nearest Neighbors (KNN), and Random Forest for Migraine Classification
Boniface Ngugi Kamau*,
Bonface Malenje,
Charity Wamwea,
Lena Anyango Onyango
Issue:
Volume 10, Issue 1, March 2025
Pages:
19-28
Received:
3 March 2025
Accepted:
14 March 2025
Published:
31 March 2025
DOI:
10.11648/j.ajmcm.20251001.13
Downloads:
Views:
Abstract: Migraine is a common neurological disorder that can seriously compromise the quality of life of the affected individuals. Migraine's typical diagnosis is solely dependent on traditional diagnostic methods which relies on patient self-reporting and clinical judgment, which can be subjective and prone to errors. The main objective of this study was to model migraine classification using Extreme Gradient Boosting (XGBoost), Random Forest, and K-Nearest Neighbors (KNN) algorithms, integrating Least Absolute Shrinkage and Selection Operator (LASSO) for feature regularization. Through this study, the classifications abilities of these machine learning models were evaluated to determine which among them is superior in terms of classifying the type of migraine one is suffering from. To prevent overfitting and enhance interpretability, LASSO regression was utilized for feature regularization. The models were trained with a labeled data set, hyperparameter tuning was achieved through Grid Search to systematically explore different combinations of hyperparameters and identify the optimal settings that maximize models performance. The models were evaluated based on accuracy, precision, recall, ROC-AUC, F1-score and computation time. The top-performing model was deployed into a web-based application using Spring Boot. XGBoost outperformed the other models, achieving an accuracy of 92.4%, an AUC of 96.0%, an F1-score of 91.65%, and a sensitivity of 92.24%, with a false positive rate of 1.59% and a computation time of 2.08s. Random Forest followed closely with 91.6% accuracy, a 94.0% AUC, an F1-score of 90.49%, and a sensitivity of 86.45%, but required 4.65s of computation time. K-Nearest Neighbors (KNN) demonstrated the lowest performance, with an accuracy of 86.6%, an AUC of 91.0%, F1-score of 80.53%, a sensitivity of 79.32%, and the highest computation time of 9.51s. XGBoost was found to be the most appropriate choice for migraine classification. This study highlights the promise of machine learning in enhancing migraine diagnosis through objective and data-driven means.
Abstract: Migraine is a common neurological disorder that can seriously compromise the quality of life of the affected individuals. Migraine's typical diagnosis is solely dependent on traditional diagnostic methods which relies on patient self-reporting and clinical judgment, which can be subjective and prone to errors. The main objective of this study was t...
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